Cross-over in scaling laws A simple example from micromagnetics
ResearchObjectives
Research ObjectivesThe MILC Collaboration is engaged in a broad research program in Quantum Chromodynamics (QCD).This research addresses fundamental questions in high energy and nuclear physics,and is directly related to major experimental programs in thesefields.It includes studies of the mass spectrum of strongly interacting particles,the weak interactions of these particles,and the behavior of strongly interacting matter under extreme conditions.The Standard Model of High Energy Physics encompasses our current knowledge of the funda-mental interactions of subatomic physics.It consists of two quantumfield theories:the Weinberg-Salaam theory of electromagnetic and weak interactions,and QCD,the theory of the strong interac-tions.The Standard Model has been enormously successful in explaining a wealth of data produced in accelerator and cosmic ray experiments over the past thirty years;however,our knowledge of it is incomplete because it has been difficult to extract many of the most interesting predictions of QCD,those that depend on the strong coupling regime of the theory,and therefore require non-perturbative calculations.At present,the only means of carrying out non-perturbative QCD calculations fromfirst principles and with controlled errors,is through large scale numerical sim-ulations within the framework of lattice gauge theory.These simulations are needed to obtain a quantitative understanding of the physical phenomena controlled by the strong interactions,to de-termine a number of the fundamental parameters of the Standard Model,and to make precise tests of the Standard Model’s range of validity.Despite the many successes of the Standard Model,it is believed by high energy physicists that to understand physics at the shortest distances,a more general theory,which unifies all four of the fundamental forces of nature,will be required.The Standard Model is expected to be a limiting case of this more general theory,just as classical mechanics is a limiting case of the more general quantum mechanics.A central objective of the experimental program in high energy physics,and of lattice QCD simulations,is to determine the range of validity of the Standard Model,and to search for new physics beyond it.Thus,QCD simulations play an important role in efforts to obtain a deeper understanding of the fundamental laws of physics.QCD is formulated in the four-dimensional space-time continuum;however,in order to carry out numerical calculations one must reformulate it on a lattice or grid.It should be emphasized that the lattice formulation of QCD is not merely a numerical approximation to the continuum formu-lation.The lattice regularization of QCD is every bit as valid as continuum regularizations.The lattice spacing a establishes a momentum cutoffπ/a that removes ultraviolet divergences.Stan-dard renormalization methods apply,and in the perturbative regime they allow a straightforward conversion of lattice results to any of the standard continuum regularization schemes.Lattice QCD calculations proceed in two steps.In thefirst,one uses importance sampling tech-niques to generate gauge configurations,which are representative samples from the Feynman path integrals that define QCD.These configurations are saved,and in the second step they are used to calculate a wide variety of physical quantities.It is necessary to generate configurations with a range of lattice spacings,and then perform extrapolations to the zero lattice spacing limit.Fur-thermore,the computational cost of calculations rises as the masses of the quarks,the fundamental constituents of strongly interacting matter,decrease.Until recently,it has been too expensive to carry out calculations with the masses of the two lightest quarks,the up and the down,set to their physical values.Instead,one has performed calculations for a range of up and down quark masses, and extrapolated to their physical values guided by chiral perturbation theory,an effectivefield theory that determines how physical quantities depend on the masses of the lightest quarks.The extrapolations in lattice spacing(continuum extrapolation)and quark mass(chiral extrapolation) are the major sources of systematic errors in QCD calculations,and both must be under control in order to obtain trustworthy results.In our current simulations,we are,for thefirst time,working at or near the physical masses of the up and down quarks.The gauge configurations produced in these simulations greatly reduce,and will eventually eliminate,the systematic errors associatedwith the chiral extrapolation.A number of different formulations of QCD on the lattice are currently in use by lattice gauge theorists,all of which are expected to give the same results in the continuum limit.In recent years, major progress has been made in thefield through the development of improved formulations(im-proved actions)which reducefinite lattice spacing artifacts.Approximately twelve years ago,we developed one such improved action called asqtad[1],which significantly increased the accuracy of our simulations for a given amount of computing resources.We have used the asqtad action to generate an extensive library of gauge configurations with small enough lattice spacings and light enough quark masses to perform controlled calculations of a number of physical quantities. Computational resources provided by the DOE and NSF have enabled us to complete our program of generating asqtad gauge configurations.These configurations are publicly available,and have been used by us and by other groups to study a wide range of physical phenomena of importance in high energy and nuclear physics.Ours was thefirst set of full QCD ensembles that enabled control over both the continuum and chiral extrapolations.We have published a review paper describing the asqtad ensembles and the many calculations that were performed with them up to2009[2]. Over the last decade,a major component of our work has been to use our asqtad gauge config-urations to calculate quantities of importance to experimental programs in high energy physics. Particular emphasis was placed on the study of the weak decays and mixings of strongly interact-ing particles in order to determine some of the least well known parameters of the standard model and to provide precise tests of the standard model.The asqtad ensembles have enabled the calcu-lation of a number of physical quantities to a precision of1%–5%,and will enable many more quantities to be determined to this precision in the coming years.These results are already having an impact on experiments in high energy physics;however,in some important calculations,partic-ularly those related to tests of the standard model,higher precision is needed than can be provided by the existing asqtad ensembles.In order to obtain the required precision,we are now working with the Highly Improved Staggered Quark(HISQ)action developed by the HPQCD Collabora-tion[3].We have performed tests of scaling in the lattice spacing using HISQ valence quarks with gauge configurations generated with HISQ sea quarks[4].We found that lattice artifacts for the HISQ action are reduced by approximately a factor of2.5from those of the asqtad action for the same lattice spacing,and taste splittings in the pion masses are reduced by approximately a factor of three,which is sufficient to enable us to undertake simulations with the mass of the Goldstone pion at or near the physical pion mass.(“Taste”refers to the different ways one can construct the same physical particle in the staggered quark formalism.Although particles with different tastes become identical in the continuum limit,their masses can differ atfinite lattice spacing).More-over,the improvement in the quark dispersion relation enables us to include charm sea quarks in the simulations.The properties of the HISQ ensembles are described in detail in Ref.[5],and the first physics calculations using the physical quark mass ensembles in Refs.[6,7,8].The current status of the HISQ ensemble generation project is described at the link HISQ Lattice Generation and some initial calculations with them at Recent Results.The HISQ action also has major advan-tages for the study of QCD at high temperatures,so we have started to use it in our studies of this subject.Projects using the HISQ action will be a major component of our research for the next several years.Our research is currently focused on three major areas:1)the properties of light pseudoscalar mesons,2)the decays and mixings of heavy-light mesons,3)the properties of strongly interacting matter at high temperatures.We briefly discuss our research in each of these areas at the link Recent Results.References[1]The MILC Collaboration:C.Bernard et al.,Nucl.Phys.(Proc.Suppl.),60A,297(1998);Phys.Rev.D58,014503(1998);G.P.Lepage,Nucl.Phys.(Proc.Suppl.),60A,267(1998);Phys.Rev.D59,074501(1999);Kostas Orginos and Doug Toussaint(MILC),Nucl.Phys.(Proc.Suppl.),73,909(1999);Phys.Rev.D59,014501(1999);Kostas Orginos,Doug Tou-ssaint and R.L.Sugar(MILC),Phys.Rev.D60,054503(1999);The MILC Collaboration:C.Bernard et al.,Phys.Rev.D61,111502(2000).[2]The MILC Collaboration: A.Bazavov et al.,Rev.Mod.Phys.82,1349-1417(2010)[arXiv:0903.3598[hep-lat]].[3]The HPQCD/UKQCD Collaboration: E.Follana et al.,Phys.Rev.D73,054502(2007)[arXiv:hep-lat/0610092].[4]The MILC Collaboration: A.Bazavov al.,Phys.Rev.D82,074501(2010)[arXiv:1004.0342].[5]The MILC Collaboration: A.Bazavov al.,Phys.Rev.D87,054505(2013)[arXiv:1212.4768].[6]The MILC Collaboration: A.Bazavov et al.,Phys.Rev.Lett.110,172003(2013)[arXiv:1301.5855].[7]The Fermilab Lattice and MILC Collaborations:A.Bazavov,et al.,Phys.Rev.Lett.112,112001(2014)[arXiv:1312.1228].[8]The MILC Collaboration:A.Bazavov et al.,Proceedings of Science(Lattice2013)405(2013)[arXiv:1312.0149].。
计量经济学中英文词汇对照
cross-loading Cross-over design Cross-section analysis Cross-section survey
Cross-sectional
Crosstabs Cross-tabulation table Cube root Cumulative distribution function Cumulative probability Curvature Curvature Curve fit Curve fitting Curvilinear regression Curvilinear relation Cut-and-try method Cycle
Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covariance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Cyclist DDD D test Data acquisition Data bank Data capacity Data deficiencies Data handling Data manipulation Data processing Data reduction Data set Data sources Data transformation Data validity Data-in Data-out Dead time Degree of freedom Degree of precision Degree of reliability Degression Density function Density of data points Dependent variable Dependent variable Depth Derivative matrix Derivative-free methods Design Determinacy Determinant Determinant Deviation Deviation from average Diagnostic plot Dichotomous variable Differential equation Direct standardization Discrete variable DISCRIMINANT Discriminant analysis Discriminant coefficient
统计学专业英语词汇完整版(可编辑修改word版)
统计学专业英语词汇AAbsolute deviation,绝对离差Absolute number,绝对数Absolute residuals,绝对残差Acceleration array,加速度立体阵Acceleration in an arbitrary direction,任意方向上的加速度Acceleration normal,法向加速度Acceleration space dimension,加速度空间的维数Acceleration tangential,切向加速度Acceleration vector,加速度向量Acceptable hypothesis,可接受假设Accumulation,累积Accuracy,准确度Actual frequency,实际频数Adaptive estimator,自适应估计量Addition,相加Addition theorem,加法定理Additivity,可加性Adjusted rate,调整率Adjusted value,校正值Admissible error,容许误差Aggregation,聚集性Alternative hypothesis,备择假设Among groups,组间Amounts,总量Analysis of correlation,相关分析Analysis of covariance,协方差分析Analysis of regression,回归分析Analysis of time series,时间序列分析Analysis of variance,方差分析Angular transformation,角转换ANOVA(analysis of variance),方差分析ANOVA Models,方差分析模型Arcing,弧/弧旋Arcsine transformation,反正弦变换Area under the curve,曲线面积AREG,评估从一个时间点到下一个时间点回归相关时的误差ARIMA,季节和非季节性单变量模型的极大似然估计Arithmetic grid paper,算术格纸Arithmetic mean,算术平均数Arrhenius relation,艾恩尼斯关系Assessing fit,拟合的评估Associative laws,结合律Asymmetric distribution,非对称分布Asymptotic bias,渐近偏倚Asymptotic efficiency,渐近效率Asymptotic variance,渐近方差Attributable risk,归因危险度Attribute data,属性资料Attribution,属性Autocorrelation,自相关Autocorrelation of residuals,残差的自相关Average,平均数Average confidence interval length,平均置信区间长度Average growth rate,平均增长率BBar chart,条形图Bar graph,条形图Base period,基期Bayes theorem, 贝叶斯定理Bell-shaped curve,钟形曲线Bernoullidistribution,伯努力分布Best-trim estimator,最好切尾估计量Bias,偏性Binary logistic regression,二元逻辑斯蒂回归Binomial distribution,二项分布Bisquare,双平方Bivariate Correlate,二变量相关Bivariate normal distribution,双变量正态分布Bivariate normal population,双变量正态总体Biweight interval,双权区间Biweight M-estimator,双权M 估计量Block,区组/配伍组BMDP(Biomedical computer programs),BMDP 统计软件包Box plots,箱线图/箱尾图Break down bound,崩溃界/崩溃点CCanonical correlation,典型相关Caption,纵标目Case-control study,病例对照研究Categorical variable,分类变量Catenary,悬链线Cauchy distribution,柯西分布Cause-and-effect relationship,因果关系Cell,单元Censoring,终检Center of symmetry,对称中心Centering and scaling,中心化和定标Central tendency,集中趋势Central value,中心值CHAID-χ2AutomaticInteractionDetector,卡方自动交互检测Chance,机遇Chance error,随机误差Chance variable,随机变量Characteristic equation,特征方程Characteristic root,特征根Characteristic vector,特征向量Chebshev criterion of fit,拟合的切比雪夫准则Chernoff faces,切尔诺夫脸谱图Chi-square test,卡方检验/χ2 检验Choleskey decomposition,乔洛斯基分解Circle chart,圆图Class interval,组距Class mid-value,组中值Class upper limit,组上限Classified variable,分类变量Cluster analysis,聚类分析Cluster sampling,整群抽样Code,代码Coded data,编码数据Coding,编码Coefficient of contingency,列联系数Coefficientof determination,决定系数Coefficient ofmultiple correlation,多重相关系数Coefficient ofpartial correlation,偏相关系数Coefficient of production-moment correlation,积差相关系数Coefficient of rank correlation,等级相关系数Coefficient of regression,回归系数Coefficient of skewness,偏度系数Coefficient of variation,变异系数Cohort study,队列研究Column,列Column effect,列效应Column factor,列因素Combination pool,合并Combinative table,组合表Common factor,共性因子Common regression coefficient,公共回归系数Common value,共同值Common variance,公共方差Common variation,公共变异Communality variance,共性方差Comparability,可比性Comparison of bathes,批比较Comparison value,比较值Compartment model,分部模型Compassion,伸缩Complement of an event,补事件Complete association,完全正相关Complete dissociation,完全不相关Complete statistics,完备统计量Completely randomized design,完全随机化设计Composite event,联合事件/复合事件Concavity,凹性Conditional expectation,条件期望Conditional likelihood,条件似然Conditional probability,条件概率Conditionally linear,依条件线性Confidence interval,置信区间Confidence limit,置信限Confidence lower limit,置信下限Confidence upper limit,置信上限Confirmatory Factor Analysis,验证性因子分析Confirmatory research,证实性实验研究Confounding factor,混杂因素Conjoint,联合分析Consistency,相合性Consistency check,一致性检验Consistent asymptotically normal estimate,相合渐近正态估计Consistent estimate,相合估计Constrained nonlinear regression,受约束非线性回归Constraint,约束Contaminated distribution,污染分布Contaminated Gausssian,污染高斯分布Contaminated normal distribution,污染正态分布Contamination,污染Contamination model,污染模型Contingency table,列联表Contour,边界线Contribution rate,贡献率Control,对照Controlled experiments,对照实验Conventional depth,常规深度Convolution,卷积Corrected factor,校正因子Corrected mean,校正均值Correction coefficient,校正系数Correctness,正确性Correlation coefficient,相关系数Correlation index,相关指数Correspondence, 对应Counting,计数Counts,计数/频数Covariance,协方差Covariant,共变Cox Regression, Cox 回归Criteria for fitting,拟合准则Criteria of least squares,最小二乘准则Critical ratio,临界比Critical region,拒绝域Critical value,临界值Cross-over design,交叉设计Cross-section analysis,横断面分析Cross-section survey,横断面调查Cross tabs,交叉表Cross-tabulation table,复合表Cube root,立方根Cumulative distribution function,累计分布函数Cumulative probability,累计概率Curvature,曲率/弯曲Curve fit,曲线拟和Curve fitting,曲线拟合Curvilinear regression,曲线回归Curvilinear relation,曲线关系Cut-and-try method,尝试法Cycle,周期Cyclist,周期性DD test, D 检验Data acquisition,资料收集Databank,数据库Data capacity,数据容量Data deficiencies,数据缺乏Data handling,数据处理Data manipulation,数据处理Data processing,数据处理Data reduction,数据缩减Data set,数据集Data sources,数据来源Data transformation,数据变换Data validity,数据有效性Data-in,数据输入Data-out,数据输出Dead time,停滞期Degree of freedom,自由度Degree of precision,精密度Degree of reliability,可靠性程度Degression,递减Density function,密度函数Density of datapoints,数据点的密度Dependent variable,应变量/依变量/因变量Depth,深度Derivative matrix,导数矩阵Derivative-free methods,无导数方法Design,设计Determinacy,确定性Determinant,行列式Determinant,决定因素Deviation,离差Deviation from average,离均差Diagnostic plot,诊断图Dichotomousvariable,二分变量Differentialequation,微分方程Directstandardization,直接标准化法Discrete variable,离散型变量Discriminant,判断Discriminant analysis,判别分析Discriminant coefficient,判别系数Discriminant function,判别值Dispersion,散布/分散度Disproportional,不成比例的Disproportionate sub-class numbers,不成比例次级组含量Distribution free,分布无关性/免分布Distribution shape,分布形状Distribution-free method,任意分布法Distributive laws,分配律Disturbance,随机扰动项Dose response curve,剂量反应曲线Double blind method,双盲法Doubleblind rial,双盲试验Double exponential distribution,双指数分布Double logarithmic,双对数Downward rank,降秩Dual-space plot,对偶空间图DUD,无导数方法Duncan's new multiple range method,新复极差法/Duncan 新法EEffect, 实验效应Eigen value,特征值Eigen vector,特征向量Ellipse,椭圆Empirical distribution,经验分布Empirical probability,经验概率单位Enumeration data,计数资料Equal sun-class number,相等次级组含量Equally likely,等可能Equal variance,同变性Error,误差/错误Error of estimate,估计误差Error type I,第一类错误Error type II,第二类错误Estimand,被估量Estimated error mean squares,估计误差均方Estimated error sum of squares,估计误差平方和Euclidean distance,欧式距离Event,事件Exceptional data point,异常数据点Expectation plane,期望平面Expectation surface,期望曲面Expected values,期望值Experiment,实验Experimental sampling,试验抽样Experimental unit,试验单位Explanatory variable,说明变量/解释变量Exploratory data analysis,探索性数据分析Explore Summarize,探索-摘要Exponential curve,指数曲线Exponential growth,指数式增长Exsooth,指数平滑方法Extended fit,扩充拟合Extra parameter,附加参数Extra polation,外推法Extreme observation,末端观测值Extremes,极端值/极值FF distribution, F 分布F test, F 检验Factor,因素/因子Factor analysis,因子分析Factor score,因子得分Factorial,阶乘Factorial design,析因试验设计False negative,假阴性False negative error,假阴性错误Family of distributions,分布族Family of estimators,估计量族Fanning,扇面Fatality rate,病死率Field investigation,现场调查Field survey,现场调查Finitepopulation,有限总体Finite-sample, 有限样本Firstderivative,一阶导数First principal component,第一主成分First quartile,第一四分位数Fisher information,费雪信息量Fitted value,拟合值Fitting a curve,曲线拟合Fixed base,定基Fluctuation,随机起伏Forecast,预测Four fold table,四格表Fourth, 四分点Fraction blow,左侧比率Fractional error,相对误差Frequency,频率Frequency polygon,频数多边图Frontier point,界限点Function relationship,泛函关系GGamma distribution,伽玛分布Gauss increment,高斯增量Gaussian distribution,高斯分布/正态分布Gauss-Newton increment,高斯-牛顿增量General census,全面普查GENLOG(Generalized liner models),广义线性模型Geometric mean,几何平均数Gini's mean difference,基尼均差GLM(General liner models),通用线性模型Goodness of fit,拟和优度/配合度Gradientof determinant,行列式的梯度Graeco-Latin square,希腊拉丁方Grand mean,总均值Gross errors,重大错误Gross-error sensitivity,大错敏感度Group averages,分组平均Grouped data,分组资料Guessed mean,假定平均数HHalf-life,半衰期Hampel M-estimators,汉佩尔M 估计量Happenstance,偶然事件Harmonic mean,调和均数Hazard function,风险均数Hazard rate,风险率Heading,标目Heavy-tailed distribution,重尾分布Hessian array,海森立体阵Heterogeneity,不同质Heterogeneity of variance,方差不齐Hierarchical classification,组内分组Hierarchical clustering method,系统聚类法High-leverage point,高杠杆率点HILOGLINEAR,多维列联表的层次对数线性模型Hinge,折叶点Histogram,直方图Historical cohort study,历史性队列研究Holes,空洞HOMALS,多重响应分析Homogeneity of variance,方差齐性Homogeneity test,齐性检验Huber M-estimators,休伯M 估计量Hyperbola,双曲线Hypothesis testing,假设检验Hypothetical universe,假设总体IImpossible event,不可能事件Independence,独立性Independent variable,自变量Index,指标/指数Indirect standardization,间接标准化法Individual,个体Inference band, 推断带Infinite population,无限总体Infinitely great, 无穷大Infinitely small,无穷小Influence curve,影响曲线Information capacity,信息容量Initial condition,初始条件Initial estimate,初始估计值Initial level,最初水平Interaction,交互作用Interaction terms,交互作用项Intercept,截距Interpolation,内插法Inter quartile range,四分位距Interval estimation,区间估计Intervals of equal probability,等概率区间Intrinsic curvature,固有曲率Invariance, 不变性Inverse matrix,逆矩阵Inverse probability,逆概率Inverse sine transformation,反正弦变换Iteration,迭代JJacobian determinant,雅可比行列式Joint distribution function,联合分布函数Joint probability,联合概率Joint probability distribution,联合概率分布KK means method,逐步聚类法Kaplan-Meier,评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier 图Kendall's rank correlation, Kendall 等级相关Kinetic,动力学Kolmogorov-Smirnove test,柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal 及Wallis 检验/多样本的秩和检验/H 检验Kurtosis,峰度LLack of fit,失拟Ladder of powers,幂阶梯Lag,滞后Large sample,大样本Large sample test,大样本检验Latin square,拉丁方Latin square design,拉丁方设计Leakage,泄漏Least favorable configuration,最不利构形Least favorable distribution,最不利分布Least significant difference,最小显著差法Least square method,最小二乘法Least-absolute-residuals estimates,最小绝对残差估计Least-absolute-residuals fit,最小绝对残差拟合Least-absolute-residuals line,最小绝对残差线Legend,图例L-estimator,L 估计量L-estimator of location,位置L 估计量L-estimator of scale,尺度L 估计量Level,水平Life expectance,预期期望寿命Life table,寿命表Life table method,生命表法Light-taile distribution,轻尾分布Likelihood function,似然函数Likelihood ratio,似然比Line graph,线图Linear correlation,直线相关Linear equation,线性方程Linear programming,线性规划Linear regression,直线回归/线性回归Linear trend,线性趋势Loading,载荷Location and scale equi variance,位置尺度同变性Location equi variance,位置同变性Location invariance,位置不变性Location scale family,位置尺度族Log rank test,时序检验Logarithmic curve,对数曲线Logarithmic normal distribution,对数正态分布Logarithmic scale,对数尺度Logarithmic transformation,对数变换Logic check,逻辑检查Logistic distribution,逻辑斯蒂分布Logit transformation, Logit 转换LOGLINEAR,多维列联表通用模型Lognormal distribution,对数正态分布Lost function,损失函数Low correlation,低度相关Lower limit,下限Lowest-attained variance,最小可达方差LSD,最小显著差法的简称Lurking variable,潜在变量MMain effect,主效应Major heading,主辞标目Marginal density function,边缘密度函数Marginal probability,边缘概率Marginal probability distribution,边缘概率分布Matched data,配对资料Matched distribution,匹配过分布Matching of distribution,分布的匹配Matching of transformation,变换的匹配Mathematical expectation,数学期望Mathematical model,数学模型MaximumL-estimator,极大L 估计量Maximumlikelihood method,最大似然法Mean,均数Mean squares between groups,组间均方Mean squares within group,组内均方Means (Compare means),均值-均值比较Median,中位数Median effective dose,半数效量Median lethal dose,半数致死量Median polish,中位数平滑Median test,中位数检验Minimal sufficient statistic,最小充分统计量Minimum distance estimation,最小距离估计Minimum effective dose,最小有效量Minimum lethal dose,最小致死量Minimum variance estimator,最小方差估计量MINITAB,统计软件包Minor heading,宾词标目Missing data,缺失值Model specification,模型的确定Modeling Statistics ,模型统计Models for outliers,离群值模型Modifying the model,模型的修正Modulus of continuity,连续性模Morbidity,发病率Most favorable configuration,最有利构形Multidimensional Scaling (ASCAL),多维尺度/多维标度Multinomial Logistic Regression ,多项逻辑斯蒂回归Multiple comparison,多重比较Multiple correlation ,复相关Multiple covariance,多元协方差Multiple linear regression,多元线性回归Multiple response ,多重选项Multiple solutions,多解Multiplication theorem,乘法定理Multiresponse,多元响应Multi-stage sampling,多阶段抽样Multivariate T distribution,多元T 分布Mutual exclusive,互不相容Mutual independence,互相独立NNatural boundary,自然边界Natural dead,自然死亡Natural zero,自然零Negative correlation,负相关Negative linear correlation,负线性相关Negatively skewed,负偏Newman-Keuls method, q 检验NK method, q 检验No statistical significance,无统计意义Nominal variable,名义变量Nonconstancy of variability,变异的非定常性Nonlinear regression,非线性相关Nonparametric statistics,非参数统计Nonparametric test,非参数检验Normal deviate,正态离差Normal distribution,正态分布Normal equation,正规方程组Normal ranges,正常范围Normal value,正常值Nuisance parameter,多余参数/讨厌参数Null hypothesis,无效假设Numerical variable,数值变量OObjective function,目标函数Observation unit,观察单位Observed value, 观察值One sided test,单侧检验One-way analysis of variance,单因素方差分析One way ANOVA ,单因素方差分析Open sequential trial,开放型序贯设计Optrim, 优切尾Optrim efficiency,优切尾效率Order statistics,顺序统计量Ordered categories,有序分类Ordinal logistic regression ,序数逻辑斯蒂回归Ordinal variable,有序变量Orthogonal basis,正交基Orthogonal design,正交试验设计Orthogonality conditions,正交条件ORTHOPLAN,正交设计Outlier cutoffs,离群值截断点Outliers,极端值OVERALS ,多组变量的非线性正规相关Overshoot,迭代过度PPaired design,配对设计Paired sample,配对样本Pairwise slopes,成对斜率Parabola,抛物线Parallel tests,平行试验Parameter,参数Parametric statistics,参数统计Parametric test,参数检验Partial correlation,偏相关Partial regression,偏回归Partial sorting,偏排序Partials residuals,偏残差Pattern,模式Pearson curves,皮尔逊曲线Peeling,退层Percent bar graph,百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves,百分位曲线Periodicity,周期性Permutation,排列P-estimator,P 估计量Pie graph,饼图Pitman estimator,皮特曼估计量Pivot,枢轴量Planar,平坦Planar assumption,平面的假设PLANCARDS,生成试验的计划卡Point estimation,点估计Poisson distribution,泊松分布Polishing,平滑Polled standard deviation,合并标准差Polled variance,合并方差Polygon,多边图Polynomial,多项式Polynomial curve,多项式曲线Population,总体Population attributable risk,人群归因危险度Positive correlation,正相关Positively skewed,正偏Posterior distribution,后验分布Power of a test,检验效能Precision,精密度Predicted value,预测值Preliminary analysis,预备性分析Principalcomponent analysis,主成分分析Priordistribution,先验分布Prior probability,先验概率Probabilistic model,概率模型probability,概率Probability density,概率密度Product moment,乘积矩/协方差Profile trace,截面迹图Proportion,比/构成比Proportion allocation in stratified random sampling,按比例分层随机抽样Proportionate,成比例Proportionate sub-class numbers,成比例次级组含量Prospective study,前瞻性调查Proximities, 亲近性Pseudo F test,近似F 检验Pseudo model,近似模型Pseudo sigma,伪标准差Purposive sampling,有目的抽样QQR decomposition, QR 分解Quadratic approximation,二次近似Qualitative classification,属性分类Qualitative method,定性方法Quantile-quantile plot,分位数-分位数图/Q-Q 图Quantitative analysis,定量分析Quartile,四分位数Quick Cluster,快速聚类RRadix sort,基数排序Random allocation,随机化分组Random blocks design,随机区组设计Random event,随机事件Randomization,随机化Range,极差/全距Rank correlation,等级相关Rank sum test,秩和检验Rank test,秩检验Ranked data,等级资料Rate,比率Ratio,比例Raw data,原始资料Rawresidual,原始残差Rayleigh's test,雷氏检验Rayleigh's Z,雷氏Z 值Reciprocal,倒数Reciprocal transformation,倒数变换Recording,记录Redescending estimators,回降估计量Reducing dimensions,降维Re-expression,重新表达Reference set,标准组Regionof acceptance,接受域Regression coefficient,回归系数Regression sum of square,回归平方和Rejection point,拒绝点Relative dispersion,相对离散度Relative number,相对数Reliability,可靠性Reparametrization,重新设置参数Replication,重复Report Summaries,报告摘要Residual sum of square,剩余平方和Resistance,耐抗性Resistant line,耐抗线Resistant technique,耐抗技术R-estimator of location,位置R 估计量R-estimator of scale,尺度R 估计量Retrospective study,回顾性调查Ridge trace,岭迹Ridit analysis , Ridit 分析Rotation, 旋转Rounding,舍入Row,行Row effects,行效应Row factor,行因素RXC table, RXC 表SSample,样本Sample regression coefficient,样本回归系数Sample size,样本量Sample standard deviation,样本标准差Sampling error,抽样误差SAS(Statistical analysis system ),SAS 统计软件包Scale,尺度/量表Scatter diagram,散点图Schematic plot,示意图/简图Score test,计分检验Screening,筛检SEASON, 季节分析Second derivative,二阶导数Second principal component,第二主成分SEM (Structural equation modeling),结构化方程模型Semi-logarithmic graph,半对数图Semi-logarithmic paper,半对数格纸Sensitivity curve,敏感度曲线Sequential analysis,贯序分析Sequential data set,顺序数据集Sequential design,贯序设计Sequential method,贯序法Sequential test,贯序检验法Serial tests,系列试验Short-cut method,简捷法Sigmoid curve, S 形曲线Sign function,正负号函数Sign test,符号检验Signed rank,符号秩Significance test,显著性检验Significant figure,有效数字Simple cluster sampling,简单整群抽样Simple correlation,简单相关Simple random sampling,简单随机抽样Simple regression,简单回归simple table,简单表Sine estimator,正弦Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skeweddistribution, 偏斜分布Skewness,偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS 统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwiseregression, 逐步回归Storage,存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t 化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival,生存分析Survival rate,生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Test(检验)Test of linearity, 线性检验Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Timeseries, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Twosided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α 错误TypeII error, 二类错误/β 错误UMVU, 方差一致最小无偏估计简称Unbiasedestimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Unweightedleast squares, 未加权最小平方法Upper limit,上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W 检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran 检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W 估计量W-estimation of location, 位置W 估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z 检验Zero correlation, 零相关Z-transformation, Z 变换。
Annual and interannual (ENSO) variability of spatial scaling properties of (NDVI) in Amazonia
Annual and interannual (ENSO)variability of spatial scaling propertiesof a vegetation index (NDVI)in AmazoniaGerma ´n Poveda *,Luis F.SalazarPosgrado en Recursos Hidra ´ulicos,Escuela de Geociencias y Medio Ambiente,Universidad Nacional de Colombia,Medellı´n,ColombiaReceived 15January 2004;received in revised form 3August 2004;accepted 5August 2004AbstractThe space–time variability of the Normalized Difference Vegetation Index (NDVI)over the Amazon River basin is quantified through thebi-dimensional Fourier spectrum,and moment-scaling analysis of monthly imagery at 8km resolution,for the period July 1981–November 2002.Monthly NDVI fields exhibit power law Fourier spectra,E (k )=ck Àb ,with k denoting the wavenumber,c the prefactor,and b the scaling exponent.Fourier spectra exhibit two scaling regimes separated at approximately 29km,above which NDVI exhibit long-range spatial correlations (0b b b 2),and below which NDVI behaves like white noise in space (b g 0).Series of monthly values of c (t )and b (t )exhibit high negative correlation (À0.88,P N 0.99),which suggest their linkages in power laws,but also that E t (k )=c (t )k Àb (t ),with t the time index.Results show a significant negative simultaneous correlation (À0.82,P N 0.95)between monthly series of average precipitation over the Amazon,h P (t )i ,and scaling exponents,b (t );and high positive lagged correlation (0.63,P N 0.95),between h P (t )i and h NDVI(t +3)i .Parameters also reflect the hydrological seasonal cycle over Amazonia:during the wet season (November–March),b (t )ranges between 0.9and 1.15,while during the dry season (May–September),b (t )g 1.30.These results reflect the more (less)coherent spatial effect of the dry (wet)season over Amazonia,which translates into longer (shorter)-range spatial correlations of the NDVI field,as witnessed by higher (lower)values of b (t ).At interannual timescales,both phases of ENSO reflect on both parameters,as b (t )is higher during El Nin ˜o than during La Nin ˜a,due to the more coherent effects of El Nin ˜o-related dryness,whereas NDVI spatial variability is enhanced during La Nin ˜a,due to positive rainfall anomalies.Results from the moment-scale analysis indicate the existence of multi-scaling in the spatial variability of NDVI fields.Departures from single scaling exhibit also annual and interannual variability,which consistently reflect the effects from both phases of ENSO.Furthermore,departures from single scaling are independent of the order moment,q ,as the PDF of departures scaled by the mean collapse to a unique distribution.These results point out that ideas of spatial scaling constitute a promising framework to synthesize important hydro-ecological processes of Amazonia.D 2004Elsevier Inc.All rights reserved.Keywords:Annual and interannual variability;Spatial scaling;Amazonia1.Introduction1.1.NDVI and the hydrologic cycleSatellite information has contributed to improve our understanding of the spatial variability of hydro-climatic and ecological processes.Vegetation activity is tightlycoupled with climate,hydro-ecological fluxes,and terrain dynamics,and it controls water,energy and carbon budgets in river basins at a wide range of space–time scales.Indices of vegetation activity are constructed using satellite infor-mation of reflectance of the relevant spectral bands which enhance the contribution of vegetation.One such an index is the Normalized Difference Vegetation Index (NDVI),defined as the ratio of (NIR ÀRed)and (NIR+Red),where NIR is the surface-reflected radiation in the near-infrared band (0.73–1.1A m),and Red is the reflected radiation in the red band (0.55–0.68A m).Theoretically,NDVI takes values in the range from À1to 1,but the observed range is usually0034-4257/$-see front matter D 2004Elsevier Inc.All rights reserved.doi:10.1016/j.rse.2004.08.001*Corresponding author.School of Geosciences and Environment,Universidad Nacional de Colombia,Carrera 80x Calle 65,Bloque M2-315,Medellin AA1027,Colombia.Tel.:+5744255122;fax:+5744255003.E-mail address:gpoveda@.co (G.Poveda).Remote Sensing of Environment 93(2004)391–401smaller,with values around0for bare soil(low or no vegetation),and values of0.9or larger for dense vegetation. The work of Tucker(1979)pioneered the study of vegetation dynamics using red and near infrared spectral measurements.Sellers(1985)showed that NDVI is directly related to the photosynthetic capacity of plant canopies, which explains why NDVI is highly and directly correlated to the intercepted fraction of photosynthetically active radiation.As such,NDVI is independent of solar radiation, although variations in solar radiation can affect retrievals of NDVI.The meaning of diverse spectral vegetation indices is explained and summarized in Myneni et al.(1995).As NDVI represents the photosynthetic capacity or photosynthetic active radiation(PAR)absorption by green leaves,it is associated with fundamental hydro-ecological processes such as precipitation,which in turn is also directly linked to photosynthesis and hence plant growth.A recent work by Lotsch et al.(2003b)provides a comprehensive global analysis of NDVI and precipitation.Other variables pertaining to the hydrologic cycle have also been linked to NDVI,such as evaporation(Szilagyi et al.,1998;Lotsch et al.,2003b),and soil moisture(Nicholson&Farrar,1994; Farrar et al.,1994;Poveda et al.,2001).A strong relation-ship between evapotranspiration and NDVI have been identified in wet environments by Seevers and Ottmann (1994),and Nicholson et al.(1996),but also in water-limited environments,as reported by Tucker and Choudhury (1987),Malo and Nicholson(1990),Nicholson et al.(1994), Grist et al.(1997),Szilagyi et al.(1998),and Lotsch et al. (2003a).Changes in vegetation patterns have been studied at a global scale through NDVI estimates(Lucht et al., 2002).In turn,Nemani et al.(2003)identify those regions of the world where primary production is limited by water,by temperature or by both.1.2.Physical settingThe Amazon River basin provides an excellent example of the coupling and feedbacks in the land surface–atmos-phere system,due to its area larger than6.4million km2, constitute largest in the world,its tropical setting,and complex eco-hydro-climatological dynamics that exert a global influence.Scientific research towards understanding the hydro-climatic and ecological functioning of the Amazon is currently undergoing within the b Large-Scale Atmos-phere–Biosphere Experiment in Amazonia Q(LBA)(see Avissar and Nobre,2002;Roberts et al.,2003).Both observations and modelling results suggest strong changes in global,regional and local atmospheric circulation patterns associated with deforestation or perturbations in the land surface–atmosphere interactions over the Amazon(Salati& V ose,1984;Silva Dias et al.,1987;Zeng et al.,1996;Zhang et al.,1996;Poveda&Mesa,1997;Marengo&Nobre,2001; Werth&Avissar,2002;Nobre et al.,2004).The seasonal cycle of precipitation exhibits a wet season during Novem-ber–March and a dry season during May–September,as a result of the latitudinal migration of the Intertropical Convergence Zone(Obregon&Nobre,1990;Zeng,1999), which interacts with the seasonal cycle of moisture-laden low level winds from the Atlantic Ocean,but also with complex feedbacks of the land surface–atmosphere system,including the significant role of evapotranspiration in precipitation recycling(Salati,1985;Eltahir&Bras,1994).This work aims to quantify how the spatial statistics of NDVI reflect the seasonal hydro-climatic variability of the Amazon.1.3.Interannual variability at ENSO timescaleAt interannual timescales,tropical South America exhib-its coherent hydro-climatic anomalies during both phases of the El Nin˜o/Southern Oscillation(ENSO)(Aceituno,1988; Kiladis&Diaz,1989;Chu,1991;Marengo&Hastenrath, 1993;Ropelewski&Halpert,1996;Poveda et al.,2001; Waylen&Poveda,2002).With minor regional exceptions in timing and amplitude,the region experiences negative anomalies in rainfall,river discharges,and soil moisture during the warm phase of ENSO(El Nin˜o),and positive anomalies during the cold phase(La Nin˜a).Both large-scale forcing and land surface hydrology play a key role on the dynamics of hydro-climatic effects of ENSO over the region (Marengo&Hastenrath,1993;Poveda&Mesa,1997), which lag anomalies in the tropical Pacific sea surface temperatures by several months.The ENSO signal prop-agates to the east in northern South America,leading hydrological anomalies by1month over western Colombia (Poveda&Mesa,1997)and by6–10months in the Amazon River basin(Richey et al.,1989;Chu,1991;Eagleson, 1994).Consistently,NDVI diminishes over tropical South America during the occurrence of the warm phase of ENSO (Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001). This work aims to quantify how the spatial statistics of NDVI reflect the interannual hydro-climatic variability of the Amazon,associated with both phases of ENSO.1.4.Scaling theories of hydro-ecological processesScaling theories have provided important clues towards understanding and modelling the space–time dynamics of diverse bio-geophysical processes,such as vegetation sur-face fluxes(Katul et al.,2001),tropical convective storms (Yano et al.,2001),modeling of rainfall fields through fractal,multi-scaling,and random cascade models(Lovejoy, 1981,1982;Lovejoy&Schertzer,1991,1992;Gupta& Waymire,1990;Over&Gupta,1994;Perica&Foufoula-Georgiou,1996;Foufoula-Georgiou,1998;Deidda et al., 1999;Harris et al.,2000;Jotithyangkoon et al.,2000; Nordstrom&Gupta,2003),maximum annual river flows (Gupta&Waymire,1990;Gupta&Dawdy,1995;Goodrich et al.,1997;Ogden&Dawdy,2003),infiltration in porous media(Barenblatt,1996),low river flows(Furey&Gupta, 2000),ecological processes(Tilman&Kareiva,1997; Bascompte&Sole,1998),and vegetation dynamics(HarteG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401 392et al.,1999;Milne&Cohen,1999;Milne et al.,2002).For instance,in the study of river floods,Gupta(2004)has explained how scaling statistics in maximum annual river flows can be used to test different physical hypotheses covering complex runoff dynamics on channel networks.Diverse multi-scale statistical techniques are used to characterize and quantify the scale dependence of geo-biophysical fields,including Fourier spectra,structure and moment-scale functions.These functions are easily comput-able and allow an understanding of the spatial structure of the fields over a wide range of scales.Also,the use of multi-scale functions allows one to identify the range of scales where the scale dependence of modelled and observed variability may deviate,and the range of scales where the two agree(Harris et al.,2000).Towards those ends,we estimate the bi-dimensional Fourier spectra of monthly NDVI fields over Amazonia,and quantify the time variability of its parameters,and how they reflect the time–space variability of NDVI and precipitation fields at annual and interannual timescales.By the same token,we like to investigate whether monthly NDVI fields exhibit simple of multi-scaling properties in space,and how they reflect the annual and interannual variability of NDVI. Thirdly,we investigate the time correlation between series of monthly values of c and b with average values of NDVI and precipitation over the entire Amazon,so as to encapsulate the hydro-ecological dynamics of Amazonia. The data sets and methodologies are described in Section2, while results are presented in Section3,and the conclusions are provided in Section4.2.Data sets and methodologiesWe used digital maps of monthly NDVI from the NASA Global Inventory Modeling and Mapping Studies (GIMMS NDVI),covering the period July1981through November2002.The imagery,which consists of8km spatial resolution NDVI images,was provided by C.J. Tucker and his colleagues at NASA Goddard Space Flight Center.The GIMMS NDVI database exhibits a great deal of improvements with respect to previous NDVI data sets, including corrections for:(i)residual sensor degradation and sensor intercalibration differences;(ii)distortions caused by persistent cloud cover in tropical evergreen broadleaf forests;(iii)solar zenith angle and viewing angle effects;(iv)volcanic aerosols;(v)missing data in the Northern Hemisphere during winter using interpolation; and(vi)short-term atmospheric aerosol effects,atmos-pheric water vapor effects,and cloud cover.For details of the GIMMS NDVI data set,see Pinzo´n et al.(submitted for publication).The GIMMS NDVI data set has been rescaled in such a way that the original values in theÀ1 to1range are obtained as ndvi=(NDVIÀ1)/249À0.05, with values larger than1representing water bodies or bad data.Precipitation data for the Amazon basin were obtained from the data set produced by the Earth Observing System-Amazon Project(EOSAP)developed by Instituto Nacional de Pesquisas Espaciais(INPE),Brazil,and the University of Washington,and contains gridded monthly rainfall(0.28 latitudeÂ0.28longitude),for the period1972–1992.This data set was provided by the Global Hydrology and Climate Center of NASA.For details of this data set,see http:// /.With the purpose of implementing the spatial scaling analysis,a2048km scale region was defined inside the Amazon basin.The observed NDVI field for July1981is shown in Fig.1,aggregated at a32km scale.Character-ization of the spatial scaling properties of NDVI monthly fields was performed through estimation of the bi-dimen-sional Fourier spectrum,and moment-scale analysis.A detailed description of the methods is provided in the following section.2.1.Bi-dimensional Fourier spectrumMany geophysical phenomena exhibit power law decay-ing Fourier spectra,(Korvin,1992;Mandelbrot,1998),i.e., E kðÞf kÀb¼ckÀbð1Þwith k being the wavenumber,c is the prefactor,and b is the scaling exponent.The spectral slope,b,becomes a measure of roughness(Davis et al.,1996;Harris et al., 1996),with low spectral slopes corresponding to rougher, less correlated fields.Scaling exponents in Fourier spectra contain key insights on the dynamics underlying the physics of highly complex phenomena.For instance,the well-known behavior of dissipation of kinetic energy in turbulent flows,for which E(k)~k5/3(Kolmogorov,1941, 1962;Frisch,1995),whose scaling exponent,5/3,summa-rizes the rate at which kinetic energy is gradually trans-ferred from larger to smaller spatial scales,such that the mean kinetic energy per unit mass per unit time is conserved.Many other geophysical phenomena exhibit power law Fourier spectra,whose scaling exponents reflect different types of statistical memory and the scale of fluctuation which is inherent to their space–time correla-tions(Korvin,1992;Mesa&Poveda,1993;Turcotte, 1997;Mandelbrot,1998;Yano et al.,2001).The bi-dimensional power spectrum is computed using standard2-D Fast Fourier Transform(FFT)algorithms (Press et al.,1992).The Fourier power or energy spectrum, E(k x,k y)of a two-dimensional field,is found by multiplying the2-D FFT by its complex conjugate,where k x and k y are the wavenumber components(Harris et al.,2000).To facilitate visualization and comparison,the2-D power spectra from the NDVI fields are averaged angularly about k x=k y=0to produce the isotropic energy spectrum,E(k), with k¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffik2xþk2yq.Such isotropic energy spectrum does not mean that the field is isotropic,but rather that the angularG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401393averaging about k x =k y =0integrates the anisotropy (Harris et al.,2000).2.2.Moment-scaling analysisMoment-scaling analysis allows the quantification of the spatial intermittency (roughness)of a field,and provides a test for the type of spatial (single or multi-)scaling behavior of random fields (Over &Gupta,1994;Harris et al.,2000).Statistical self-similarity can be thought of as statistical similarity of a random field across multiple scales,then simple scaling is a type of statistical self-similarity.Consider a random field,{X (t );t a I },where I represents an index set,and an arbitrary scalar k N 0.The random field is defined to be simple scaling if the following holds,X k t ðÞ¼dk h X t ðÞð2Þwhere the equality is understood in the sense of all finite dimensional distribution functions.From the definition of statistical moments given by E [X q]=R x q f (x )d x ,q =1,2,3,...,it is concluded that for a simple scaling random field,X (t ),E X q k t ðÞ½ ¼k h q E X q t ðÞ½ ;q ¼1;2;3;Nor ;log E X q k t ðÞ½ ¼q h log k þc q t ðÞ;ð3Þwhere c q (t )=log E [X q (t )].Eq.(3)shows that simple scaling must satisfy two conditions:(i)log–log linearity;(ii)linear slope growth,i.e.,s (q )=q h ,whereas multi-scaling holds for a nonlinear slope growth.In our case,the expected value in Eq.(3)arises from the equation that defines the scaling moments of a field X j ,which are computed for a range of averaging scales,r ,with higher values of r implying examining the phenomena at finer spatial resolution.Therefore,M q r ðÞ¼hj X r x ;y ðÞj q ið4Þwhere X r represents field values at scale r ,q is the order of the moment,and h ...i denotes the expected value of NDVI over all pixels at scale r .Typically,the scale of the image is dyadically reduced from its original highest resolution (r =1/(1pixel))by successive spatial averaging of the field by a factor of 2at each step,i.e.,r =1/(2pixels)=0.5,r =1/(4pixels)=0.25,...,r =1/(256pixels)=3.9Â10À3.Scaling of the moments means that (Gupta &Waymire,1993),M q r ðÞf r Às q ðÞð5Þwhere s (q )is the moment scaling exponent function that is estimated by log–log linear regressions of the q th moment of the NDVI field,on a scan by scan basis,as |X r |vs.log r ,for each q .It is easy to check that s (1)=0since the mean of the entire field does not depend upon the scale.Thelog–logFig.1.Location of the study region depicting the NDVI field for July 1981,aggregated at a 32km scale.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401394linearity of log M q (r )vs.log r provides a test of the scaling hypothesis for the moment of order q .3.Results3.1.Bi-dimensional Fourier spectrumOur generalized results indicate that the Fourier spectra exhibit two regions characterized by different scaling exponents,b ,separated at the wavenumber k =0.034km À1,which corresponds to 28.6km.Fig.2shows the 2-D Fourier spectra for the September 1989NDVI field.At larger spatial scales,the NDVI fields exhibit long-range correlations characterized by 0b b b 2,whereas for larger wavenumbers (smaller spatial scales),the spectrum becomes scale independent,with b g 0,thus meaning that the spatial variability of NDVI behaves irregularly,as white noise in space.Long-range correlations in the spatial distribution of water and energy-limited vegetation have been identified for the Columbia River basin in the USA (Milne et al.,2002).Analysis of the time evolution of monthly estimated values of scaling exponents,b (t ),and prefactors,c (t ),t =1,...,257,indicates a high negative correlation coefficient (À0.88,P N 0.95),as shown in Fig.3,which means that c (t )=f [b (t )],with f [d ]representing a linear function.This result points out to the existence of a strong association between these two parameters in power laws and scaling relationships;an idea that was introduced in the context of the Hurst effect in geophysical records (Mesa &Poveda,1993),which deserves further investigation.Furthermore,our results indicate that both parameters of the Fourier spectra vary with time,and thus E t (k )=c (t )k Àb (t ),where k denotes the wavenumber,and t represents the time index.Time series of monthly values of average precipitation and NDVI over the Amazon were estimated by averaging values of each field for a fixed month,as,h P t ðÞi ¼1=nXn i ¼1p i !t;and h NDVI t ðÞi ¼1=nX n i ¼1ndvi i!tð6Þwhere n denotes the number of pixels with information foreach field:12,991for precipitation,and 65,536for NDVI.Results show a significant negative correlation (À0.82,P N 0.95)between monthly values of average precipitation,h P (t )i over the Amazon and scaling exponents,b (t ),as illustrated in Fig.4.Such negative correlation indicates that wet months exhibit rougher (less spatially correlated)NDVI fields,which are encapsulated in lower values of b (t ).On the contrary,dry periods are associated with more coherent and longer-range correlated NDVI fields,which are reflected in higher values of b (t ).Accordingly,the afore-mentioned seasonal cycle of average precipitation,and the concomitant spatial variability of NDVI are also reflectedinFig.2.Bi-dimensional Fourier spectra of the NDVI field for September 1989,with scaling exponent,b =1.11,and prefactor,c =0.034.E (k )has arbitrary units,as NDVI is dimensionless.Values of NDVI are rescaled is such a manner that original data are recovered as ndvi=(NDVI À1)/249–0.05.The range of spatial scales covers from 512to 16km.The dotted line separates the two scaling regions of the spectrum at k =0.035km À1,which corresponds to a spatial scale of 28.6km.Fig.3.Time evolution of prefactors,c (t ),and scaling exponents,b (t ),for estimated bi-dimensional Fourier spectra of NDVI monthly fields,during the study period.Simultaneous correlation coefficient is À0.88(P N0.99).Fig. 4.Time evolution of monthly mean precipitation,h P (t )i ,over Amazonia and scaling exponents,b (t ).Simultaneous correlation is À0.82(P N 0.95).G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401395a high positive correlation coefficient (0.72,P N 0.95)between the monthly series of average precipitation,h P (t )i and that of 6-month lagged scaling exponents,hb (t +6)i (not shown here).Despite that no significant correlation (À0.062)appears between the series of h P (t )i and h NDVI(t )i ,there is a significant positive correlation at 3-month lag (0.63,P N 0.95),see Fig.5,when precipitation leads NDVI.Such time lag suggests an integrated timescale at which rainfall affects NDVI dynamics at basin scale.The physical origin of this observation lies in the complex interactions of the land surface–atmosphere system,which include the afore-mentioned important effect of precipitation recycling in Amazonia (Salati,1985;Eltahir &Bras,1994).This observation deserves further investigation.3.1.1.Annual and interannual timescalesThe average long-term annual cycle of b (t )and c (t )were estimated from the 257estimated values (July 1981–November 2002).There is a strong negatively correlated seasonal cycle of prefactors,c (t ),and scaling exponents,b (t ),as evidenced in Fig.6.During the wet season (November–March),the estimated values of b (t )lie between 0.9and 1.15,while during the dry season (May–September),higher values are on the order of b (t )=1.30.These results are explained by the more coherent spatial effects of the dry season over the Amazon basin,which produce long-range spatial correlations in the NDVI field,reflected in higher estimates of b (t ).The annual cycle of prefactors,c (t ),exhibit higher values (~0.09–0.10)during the wet season,and lower values (~0.02–0.30)during the dry season.An understanding of the physical processes that govern such a strong association between scaling exponents and prefactors at seasonal scales is a topic of further research.At interannual timescales,ENSO strongly affects vege-tation activity and NDVI variability in Amazonia (Gutman,1991;Kogan &Sullivan,1993;Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001;Pinzo ´n,2002).Fig.7shows the annual cycle of scaling exponents,b (t ),during two contrasting ENSO years,i.e.,the 1991–1992El Nin ˜o,and the 1988–1989La Nin ˜a,as well as the average during normal years.The annual cycle is defined from June (year 0)through May (year +1),to better capture the aforementioned delayed effects of both ENSO phases.Overall,results indicate that the phase of the annual cycle of b (t )remains unchanged during both phases of ENSO,but there is a clear-cut effect on its amplitude.This is evidenced by higher values of b (t )during El Nin ˜o as compared with those during La Nin ˜a and normal years,throughout the annual cycle.Interestingly enough,the highest values of the scaling exponent are attained during August for both ENSO phases,and the lower values appears in February–March during El Nin ˜o,and in November–February during La Nin ˜a.This observation can be explained by the spatially coherent dryness caused over the Amazon by the warm phase of ENSO.It is well known that,in general,the Amazon basin experiences strong droughts during El Nin ˜o,and positive rainfall anomalies during La Nin ˜a (Richey et al.,1989;Fig.5.Time evolution of average values of monthly precipitation,h P (t )i ,and NDVI h NDVI(t )i ,over Amazonia.The caption of Fig.2explains the range of NDVI values.The low simultaneous correlation coefficient (À0.06)increases to À0.63(P N 0.95),when precipitation lead values of NDVI by 3months.Fig.6.Long-term annual cycle of estimated prefactors,c (t ),and scaling exponents,b (t ),from the estimated 2-D Fourierspectra.Fig.7.Annual cycle of scaling exponents,b (t ),during the 1991–1992El Nin ˜o event,the 1988–1989La Nin ˜a event,and during normal years.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401396Marengo,1992;Marengo &Hastenrath,1993;Obregon &Nobre,1990;Poveda &Mesa,1997;Poveda et al.,2001),whose effects are stronger in northern and central Amazonia(Marengo et al.,1998).The most remarkable differences occur in the November–March wet season during both phases of ENSO.During this epoch,both ENSO phases attain their maximum amplitude,and the associated tele-connections are more strongly developed,which in con-junction with land surface–atmosphere feedbacks cause stronger hydro-ecological anomalies,which affect the NDVI response over the Amazon.Our results confirm the coherent large spatial scale effects of El Nin ˜o-related drought over the Amazon basin,as a result of the 1991–1992event.It is concluded that scaling exponents,b (t ),exhibit significant variability at ENSO timescales,which are consistent with and reflect the identified hydrological anomalies.Similar to the temporal behavior of h NDVI(t )i ,results for the scaling exponents confirm that NDVI fields are more spatially correlated during El Nin ˜o than during La Nin ˜a.The interannual variability associated with both phases of ENSO is consistently exemplified by the evolution of b (t )and c (t )during the 1997–1998El Nin ˜o event,and during the 1998–2000La Nin ˜a event,shown in Fig.3.3.2.Moment-scaling analysisMoment-scale analysis were performed after checking for the condition that b (t )b 2(Harris et al.,2000).Fig.8shows the results for July 1991,with the scaling of marginal moments with q =0.5,1.0,...,4(top),and the estimated s ðq ˆÞcurve (bottom).Results indicate that monthly NDVI fields exhibit multi-scaling behavior in space,as indicated by the nonlinear behavior of the s ðq ˆÞfunction.Deviations from simple scaling were quantified as D q ¼s ðq ˆÞobserved Às q ðÞtheoretical ,e.g.,the difference between the sample values of the function s ðq ˆÞ,with respect to the linear growth for simple scaling (see Fig.9).Two features are worth mentioning:(i)there is a clear-cut annualandFig.8.Scaling of marginal moments with q =0.5,1.0,...,4(top),and estimated s ðq ˆÞcurve (bottom),for NDVI in July 1991.The straight continuous line and 95%confidence intervals (dashed)in the bottom pannel denote the theoretical behavior for simplescaling.Fig.9.Time evolution of departures from simple scaling,of the estimated s ðq ˆÞcurve,for q =1.5,...,4.0,during the study period.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401397。
物理化学基本概念
物理化学概念及术语A B C D E F G H I J K L M N O P Q R S T U V W X Y Z概念及术语 (16)BET公式BET formula (16)DLVO理论 DLVO theory (16)HLB法hydrophile-lipophile balance method (16)pVT性质 pVT property (16)ζ电势 zeta potential (16)阿伏加德罗常数 Avogadro’number (16)阿伏加德罗定律 Avogadro law (16)阿累尼乌斯电离理论Arrhenius ionization theory (16)阿累尼乌斯方程Arrhenius equation (17)阿累尼乌斯活化能 Arrhenius activation energy (17)阿马格定律 Amagat law (17)艾林方程 Erying equation (17)爱因斯坦光化当量定律 Einstein’s law of photochemical equivalence (17)爱因斯坦-斯托克斯方程 Einstein-Stokes equation (17)安托万常数 Antoine constant (17)安托万方程 Antoine equation (17)盎萨格电导理论Onsager’s theory of conductance (17)半电池half cell (17)半衰期half time period (18)饱和液体 saturated liquids (18)饱和蒸气 saturated vapor (18)饱和吸附量 saturated extent of adsorption (18)饱和蒸气压 saturated vapor pressure (18)爆炸界限 explosion limits (18)比表面功 specific surface work (18)比表面吉布斯函数 specific surface Gibbs function (18)比浓粘度 reduced viscosity (18)标准电动势 standard electromotive force (18)标准电极电势 standard electrode potential (18)标准摩尔反应焓 standard molar reaction enthalpy (18)标准摩尔反应吉布斯函数 standard Gibbs function of molar reaction (18)标准摩尔反应熵 standard molar reaction entropy (19)标准摩尔焓函数 standard molar enthalpy function (19)标准摩尔吉布斯自由能函数 standard molar Gibbs free energy function (19)标准摩尔燃烧焓 standard molar combustion enthalpy (19)标准摩尔熵 standard molar entropy (19)标准摩尔生成焓 standard molar formation enthalpy (19)标准摩尔生成吉布斯函数 standard molar formation Gibbs function (19)标准平衡常数 standard equilibrium constant (19)标准氢电极 standard hydrogen electrode (19)标准态 standard state (19)标准熵 standard entropy (20)标准压力 standard pressure (20)标准状况 standard condition (20)表观活化能apparent activation energy (20)表观摩尔质量 apparent molecular weight (20)表观迁移数apparent transference number (20)表面 surfaces (20)表面过程控制 surface process control (20)表面活性剂surfactants (21)表面吸附量 surface excess (21)表面张力 surface tension (21)表面质量作用定律 surface mass action law (21)波义尔定律 Boyle law (21)波义尔温度 Boyle temperature (21)波义尔点 Boyle point (21)玻尔兹曼常数 Boltzmann constant (22)玻尔兹曼分布 Boltzmann distribution (22)玻尔兹曼公式 Boltzmann formula (22)玻尔兹曼熵定理 Boltzmann entropy theorem (22)泊Poise (22)不可逆过程 irreversible process (22)不可逆过程热力学thermodynamics of irreversible processes (22)不可逆相变化 irreversible phase change (22)布朗运动 brownian movement (22)查理定律 Charle’s law (22)产率 yield (23)敞开系统 open system (23)超电势 over potential (23)沉降 sedimentation (23)沉降电势 sedimentation potential (23)沉降平衡 sedimentation equilibrium (23)触变 thixotropy (23)粗分散系统 thick disperse system (23)催化剂 catalyst (23)单分子层吸附理论 mono molecule layer adsorption (23)单分子反应 unimolecular reaction (23)单链反应 straight chain reactions (24)弹式量热计 bomb calorimeter (24)道尔顿定律 Dalton law (24)道尔顿分压定律 Dalton partial pressure law (24)德拜和法尔肯哈根效应Debye and Falkenhagen effect (24)德拜立方公式 Debye cubic formula (24)德拜-休克尔极限公式 Debye-Huckel’s limiting equation (24)等焓过程 isenthalpic process (24)等焓线isenthalpic line (24)等几率定理 theorem of equal probability (24)等温等容位Helmholtz free energy (25)等温等压位Gibbs free energy (25)等温方程 equation at constant temperature (25)低共熔点 eutectic point (25)低共熔混合物 eutectic mixture (25)低会溶点 lower consolute point (25)低熔冰盐合晶 cryohydric (26)第二类永动机 perpetual machine of the second kind (26)第三定律熵 Third-Law entropy (26)第一类永动机 perpetual machine of the first kind (26)缔合化学吸附 association chemical adsorption (26)电池常数 cell constant (26)电池电动势 electromotive force of cells (26)电池反应 cell reaction (27)电导 conductance (27)电导率 conductivity (27)电动势的温度系数 temperature coefficient of electromotive force (27)电动电势 zeta potential (27)电功electric work (27)电化学 electrochemistry (27)电化学极化 electrochemical polarization (27)电极电势 electrode potential (27)电极反应 reactions on the electrode (27)电极种类 type of electrodes (27)电解池 electrolytic cell (28)电量计 coulometer (28)电流效率current efficiency (28)电迁移 electro migration (28)电迁移率 electromobility (28)电渗 electroosmosis (28)电渗析 electrodialysis (28)电泳 electrophoresis (28)丁达尔效应 Dyndall effect (28)定容摩尔热容 molar heat capacity under constant volume (28)定容温度计 Constant voIume thermometer (28)定压摩尔热容 molar heat capacity under constant pressure (29)定压温度计 constant pressure thermometer (29)定域子系统 localized particle system (29)动力学方程kinetic equations (29)动力学控制 kinetics control (29)独立子系统 independent particle system (29)对比摩尔体积 reduced mole volume (29)对比体积 reduced volume (29)对比温度 reduced temperature (29)对比压力 reduced pressure (29)对称数 symmetry number (29)对行反应reversible reactions (29)对应状态原理 principle of corresponding state (29)多方过程polytropic process (30)多分子层吸附理论 adsorption theory of multi-molecular layers (30)二级反应second order reaction (30)二级相变second order phase change (30)法拉第常数 faraday constant (31)法拉第定律 Faraday’s law (31)反电动势back E.M.F. (31)反渗透 reverse osmosis (31)反应分子数 molecularity (31)反应级数 reaction orders (31)反应进度 extent of reaction (32)反应热heat of reaction (32)反应速率rate of reaction (32)反应速率常数 constant of reaction rate (32)范德华常数 van der Waals constant (32)范德华方程 van der Waals equation (32)范德华力 van der Waals force (32)范德华气体 van der Waals gases (32)范特霍夫方程 van’t Hoff equation (32)范特霍夫规则 van’t Hoff rule (33)范特霍夫渗透压公式 van’t Hoff equation of osmotic pressure (33)非基元反应 non-elementary reactions (33)非体积功 non-volume work (33)非依时计量学反应 time independent stoichiometric reactions (33)菲克扩散第一定律 Fick’s first law of diffusion (33)沸点 boiling point (33)沸点升高 elevation of boiling point (33)费米-狄拉克统计Fermi-Dirac statistics (33)分布 distribution (33)分布数 distribution numbers (34)分解电压 decomposition voltage (34)分配定律 distribution law (34)分散系统 disperse system (34)分散相 dispersion phase (34)分体积 partial volume (34)分体积定律 partial volume law (34)分压 partial pressure (34)分压定律 partial pressure law (34)分子反应力学 mechanics of molecular reactions (34)分子间力 intermolecular force (34)分子蒸馏molecular distillation (35)封闭系统 closed system (35)附加压力 excess pressure (35)弗罗因德利希吸附经验式 Freundlich empirical formula of adsorption (35)负极 negative pole (35)负吸附 negative adsorption (35)复合反应composite reaction (35)盖.吕萨克定律 Gay-Lussac law (35)盖斯定律 Hess law (35)甘汞电极 calomel electrode (35)感胶离子序 lyotropic series (35)杠杆规则 lever rule (35)高分子溶液 macromolecular solution (36)高会溶点 upper consolute point (36)隔离法the isolation method (36)格罗塞斯-德雷珀定律 Grotthus-Draoer’s law (36)隔离系统 isolated system (37)根均方速率 root-mean-square speed (37)功 work (37)功函work content (37)共轭溶液 conjugate solution (37)共沸温度 azeotropic temperature (37)构型熵configurational entropy (37)孤立系统 isolated system (37)固溶胶 solid sol (37)固态混合物 solid solution (38)固相线 solid phase line (38)光反应 photoreaction (38)光化学第二定律 the second law of actinochemistry (38)光化学第一定律 the first law of actinochemistry (38)光敏反应 photosensitized reactions (38)光谱熵 spectrum entropy (38)广度性质 extensive property (38)广延量 extensive quantity (38)广延性质 extensive property (38)规定熵 stipulated entropy (38)过饱和溶液 oversaturated solution (38)过饱和蒸气 oversaturated vapor (38)过程 process (39)过渡状态理论 transition state theory (39)过冷水 super-cooled water (39)过冷液体 overcooled liquid (39)过热液体 overheated liquid (39)亥姆霍兹函数 Helmholtz function (39)亥姆霍兹函数判据 Helmholtz function criterion (39)亥姆霍兹自由能 Helmholtz free energy (39)亥氏函数 Helmholtz function (39)焓 enthalpy (39)亨利常数 Henry constant (39)亨利定律 Henry law (39)恒沸混合物 constant boiling mixture (40)恒容摩尔热容 molar heat capacity at constant volume (40)恒容热 heat at constant volume (40)恒外压 constant external pressure (40)恒压摩尔热容 molar heat capacity at constant pressure (40)恒压热 heat at constant pressure (40)化学动力学chemical kinetics (40)化学反应计量式 stoichiometric equation of chemical reaction (40)化学反应计量系数 stoichiometric coefficient of chemical reaction (40)化学反应进度 extent of chemical reaction (41)化学亲合势 chemical affinity (41)化学热力学chemical thermodynamics (41)化学势 chemical potential (41)化学势判据 chemical potential criterion (41)化学吸附 chemisorptions (41)环境 environment (41)环境熵变 entropy change in environment (41)挥发度volatility (41)混合熵 entropy of mixing (42)混合物 mixture (42)活度 activity (42)活化控制 activation control (42)活化络合物理论 activated complex theory (42)活化能activation energy (43)霍根-华森图 Hougen-Watson Chart (43)基态能级 energy level at ground state (43)基希霍夫公式 Kirchhoff formula (43)基元反应elementary reactions (43)积分溶解热 integration heat of dissolution (43)吉布斯-杜亥姆方程 Gibbs-Duhem equation (43)吉布斯-亥姆霍兹方程 Gibbs-Helmhotz equation (43)吉布斯函数 Gibbs function (43)吉布斯函数判据 Gibbs function criterion (44)吉布斯吸附公式Gibbs adsorption formula (44)吉布斯自由能 Gibbs free energy (44)吉氏函数 Gibbs function (44)极化电极电势 polarization potential of electrode (44)极化曲线 polarization curves (44)极化作用 polarization (44)极限摩尔电导率 limiting molar conductivity (44)几率因子 steric factor (44)计量式 stoichiometric equation (44)计量系数 stoichiometric coefficient (45)价数规则 rule of valence (45)简并度 degeneracy (45)键焓bond enthalpy (45)胶冻 broth jelly (45)胶核 colloidal nucleus (45)胶凝作用 demulsification (45)胶束micelle (45)胶体 colloid (45)胶体分散系统 dispersion system of colloid (45)胶体化学 collochemistry (45)胶体粒子 colloidal particles (45)胶团 micelle (45)焦耳Joule (45)焦耳-汤姆生实验 Joule-Thomson experiment (46)焦耳-汤姆生系数 Joule-Thomson coefficient (46)焦耳-汤姆生效应 Joule-Thomson effect (46)焦耳定律 Joule's law (46)接触电势contact potential (46)接触角 contact angle (46)节流过程 throttling process (46)节流膨胀 throttling expansion (46)节流膨胀系数 coefficient of throttling expansion (46)结线 tie line (46)结晶热heat of crystallization (47)解离化学吸附 dissociation chemical adsorption (47)界面 interfaces (47)界面张力 surface tension (47)浸湿 immersion wetting (47)浸湿功 immersion wetting work (47)精馏 rectify (47)聚(合)电解质polyelectrolyte (47)聚沉 coagulation (47)聚沉值 coagulation value (47)绝对反应速率理论 absolute reaction rate theory (47)绝对熵 absolute entropy (47)绝对温标absolute temperature scale (48)绝热过程 adiabatic process (48)绝热量热计adiabatic calorimeter (48)绝热指数 adiabatic index (48)卡诺定理 Carnot theorem (48)卡诺循环 Carnot cycle (48)开尔文公式 Kelvin formula (48)柯诺瓦洛夫-吉布斯定律 Konovalov-Gibbs law (48)科尔劳施离子独立运动定律 Kohlrausch’s Law of Independent Migration of Ions (48)可能的电解质potential electrolyte (49)可逆电池 reversible cell (49)可逆过程 reversible process (49)可逆过程方程 reversible process equation (49)可逆体积功 reversible volume work (49)可逆相变 reversible phase change (49)克拉佩龙方程 Clapeyron equation (49)克劳修斯不等式 Clausius inequality (49)克劳修斯-克拉佩龙方程 Clausius-Clapeyron equation (49)控制步骤 control step (50)库仑计 coulometer (50)扩散控制 diffusion controlled (50)拉普拉斯方程 Laplace’s equation (50)拉乌尔定律 Raoult law (50)兰格缪尔-欣谢尔伍德机理 Langmuir-Hinshelwood mechanism (50)雷利公式 Rayleigh equation (50)兰格缪尔吸附等温式 Langmuir adsorption isotherm formula (50)冷冻系数coefficient of refrigeration (50)冷却曲线 cooling curve (51)离解热heat of dissociation (51)离解压力dissociation pressure (51)离域子系统 non-localized particle systems (51)离子的标准摩尔生成焓 standard molar formation of ion (51)离子的电迁移率 mobility of ions (51)离子的迁移数 transport number of ions (51)离子独立运动定律 law of the independent migration of ions (51)离子氛 ionic atmosphere (51)离子强度 ionic strength (51)理想混合物 perfect mixture (52)理想气体 ideal gas (52)理想气体的绝热指数 adiabatic index of ideal gases (52)理想气体的微观模型 micro-model of ideal gas (52)理想气体反应的等温方程 isothermal equation of ideal gaseous reactions (52)理想气体绝热可逆过程方程 adiabatic reversible process equation of ideal gases (52)理想气体状态方程 state equation of ideal gas (52)理想稀溶液 ideal dilute solution (52)理想液态混合物 perfect liquid mixture (52)粒子 particles (52)粒子的配分函数 partition function of particles (53)连串反应consecutive reactions (53)链的传递物 chain carrier (53)链反应 chain reactions (53)量热熵 calorimetric entropy (53)量子统计quantum statistics (53)量子效率 quantum yield (53)临界参数 critical parameter (53)临界常数 critical constant (53)临界点 critical point (53)临界胶束浓度critical micelle concentration (53)临界摩尔体积 critical molar volume (54)临界温度 critical temperature (54)临界压力 critical pressure (54)临界状态 critical state (54)零级反应zero order reaction (54)流动电势 streaming potential (54)流动功 flow work (54)笼罩效应 cage effect (54)路易斯-兰德尔逸度规则 Lewis-Randall rule of fugacity (54)露点 dew point (54)露点线 dew point line (54)麦克斯韦关系式 Maxwell relations (55)麦克斯韦速率分布 Maxwell distribution of speeds (55)麦克斯韦能量分布 MaxwelIdistribution of energy (55)毛细管凝结 condensation in capillary (55)毛细现象 capillary phenomena (55)米凯利斯常数 Michaelis constant (55)摩尔电导率 molar conductivity (56)摩尔反应焓 molar reaction enthalpy (56)摩尔混合熵 mole entropy of mixing (56)摩尔气体常数 molar gas constant (56)摩尔热容 molar heat capacity (56)摩尔溶解焓 mole dissolution enthalpy (56)摩尔稀释焓 mole dilution enthalpy (56)内扩散控制 internal diffusions control (56)内能 internal energy (56)内压力 internal pressure (56)能级 energy levels (56)能级分布 energy level distribution (57)能量均分原理 principle of the equipartition of energy (57)能斯特方程 Nernst equation (57)能斯特热定理 Nernst heat theorem (57)凝固点 freezing point (57)凝固点降低 lowering of freezing point (57)凝固点曲线 freezing point curve (58)凝胶 gelatin (58)凝聚态 condensed state (58)凝聚相 condensed phase (58)浓差超电势 concentration over-potential (58)浓差极化 concentration polarization (58)浓差电池 concentration cells (58)帕斯卡pascal (58)泡点 bubble point (58)泡点线 bubble point line (58)配分函数 partition function (58)配分函数的析因子性质 property that partition function to be expressed as a product of the separate partition functions for each kind of state (58)碰撞截面 collision cross section (59)碰撞数 the number of collisions (59)偏摩尔量 partial mole quantities (59)平衡常数(理想气体反应) equilibrium constants for reactions of ideal gases (59)平动配分函数 partition function of translation (59)平衡分布 equilibrium distribution (59)平衡态 equilibrium state (60)平衡态近似法 equilibrium state approximation (60)平衡状态图 equilibrium state diagram (60)平均活度 mean activity (60)平均活度系统 mean activity coefficient (60)平均摩尔热容 mean molar heat capacity (60)平均质量摩尔浓度 mean mass molarity (60)平均自由程mean free path (60)平行反应parallel reactions (61)破乳 demulsification (61)铺展 spreading (61)普遍化范德华方程 universal van der Waals equation (61)其它功 the other work (61)气化热heat of vaporization (61)气溶胶 aerosol (61)气体常数 gas constant (61)气体分子运动论 kinetic theory of gases (61)气体分子运动论的基本方程 foundamental equation of kinetic theory of gases (62)气溶胶 aerosol (62)气相线 vapor line (62)迁移数 transport number (62)潜热latent heat (62)强度量 intensive quantity (62)强度性质 intensive property (62)亲液溶胶 hydrophilic sol (62)氢电极 hydrogen electrodes (62)区域熔化zone melting (62)热 heat (62)热爆炸 heat explosion (62)热泵 heat pump (63)热功当量mechanical equivalent of heat (63)热函heat content (63)热化学thermochemistry (63)热化学方程thermochemical equation (63)热机 heat engine (63)热机效率 efficiency of heat engine (63)热力学 thermodynamics (63)热力学第二定律 the second law of thermodynamics (63)热力学第三定律 the third law of thermodynamics (63)热力学第一定律 the first law of thermodynamics (63)热力学基本方程 fundamental equation of thermodynamics (64)热力学几率 thermodynamic probability (64)热力学能 thermodynamic energy (64)热力学特性函数characteristic thermodynamic function (64)热力学温标thermodynamic scale of temperature (64)热力学温度thermodynamic temperature (64)热熵thermal entropy (64)热效应heat effect (64)熔点曲线 melting point curve (64)熔化热heat of fusion (64)溶胶 colloidal sol (65)溶解焓 dissolution enthalpy (65)溶液 solution (65)溶胀 swelling (65)乳化剂 emulsifier (65)乳状液 emulsion (65)润湿 wetting (65)润湿角 wetting angle (65)萨克尔-泰特洛德方程 Sackur-Tetrode equation (66)三相点 triple point (66)三相平衡线 triple-phase line (66)熵 entropy (66)熵判据 entropy criterion (66)熵增原理 principle of entropy increase (66)渗透压 osmotic pressure (66)渗析法 dialytic process (67)生成反应 formation reaction (67)升华热heat of sublimation (67)实际气体 real gas (67)舒尔采-哈迪规则 Schulze-Hardy rule (67)松驰力relaxation force (67)松驰时间time of relaxation (67)速度常数reaction rate constant (67)速率方程rate equations (67)速率控制步骤rate determining step (68)塔费尔公式 Tafel equation (68)态-态反应 state-state reactions (68)唐南平衡 Donnan equilibrium (68)淌度 mobility (68)特鲁顿规则 Trouton rule (68)特性粘度 intrinsic viscosity (68)体积功 volume work (68)统计权重 statistical weight (68)统计热力学 statistic thermodynamics (68)统计熵 statistic entropy (68)途径 path (68)途径函数 path function (69)外扩散控制 external diffusion control (69)完美晶体 perfect crystalline (69)完全气体 perfect gas (69)微观状态 microstate (69)微态 microstate (69)韦斯顿标准电池 Weston standard battery (69)维恩效应Wien effect (69)维里方程 virial equation (69)维里系数 virial coefficient (69)稳流过程 steady flow process (69)稳态近似法 stationary state approximation (69)无热溶液athermal solution (70)无限稀溶液 solutions in the limit of extreme dilution (70)物理化学 Physical Chemistry (70)物理吸附 physisorptions (70)吸附 adsorption (70)吸附等量线 adsorption isostere (70)吸附等温线 adsorption isotherm (70)吸附等压线 adsorption isobar (70)吸附剂 adsorbent (70)吸附量 extent of adsorption (70)吸附热 heat of adsorption (70)吸附质 adsorbate (70)析出电势 evolution or deposition potential (71)稀溶液的依数性 colligative properties of dilute solutions (71)稀释焓 dilution enthalpy (71)系统 system (71)系统点 system point (71)系统的环境 environment of system (71)相 phase (71)相变 phase change (71)相变焓 enthalpy of phase change (71)相变化 phase change (71)相变热 heat of phase change (71)相点 phase point (71)相对挥发度relative volatility (72)相对粘度 relative viscosity (72)相律 phase rule (72)相平衡热容heat capacity in phase equilibrium (72)相图 phase diagram (72)相倚子系统 system of dependent particles (72)悬浮液 suspension (72)循环过程 cyclic process (72)压力商 pressure quotient (72)压缩因子 compressibility factor (73)压缩因子图 diagram of compressibility factor (73)亚稳状态 metastable state (73)盐桥 salt bridge (73)盐析 salting out (73)阳极 anode (73)杨氏方程 Young’s equation (73)液体接界电势 liquid junction potential (73)液相线 liquid phase lines (73)一级反应first order reaction (73)一级相变first order phase change (74)依时计量学反应 time dependent stoichiometric reactions (74)逸度 fugacity (74)逸度系数 coefficient of fugacity (74)阴极 cathode (75)荧光 fluorescence (75)永动机 perpetual motion machine (75)永久气体 Permanent gas (75)有效能 available energy (75)原电池 primary cell (75)原盐效应 salt effect (75)增比粘度 specific viscosity (75)憎液溶胶 lyophobic sol (75)沾湿 adhesional wetting (75)沾湿功 the work of adhesional wetting (75)真溶液 true solution (76)真实电解质real electrolyte (76)真实气体 real gas (76)真实迁移数true transference number (76)振动配分函数 partition function of vibration (76)振动特征温度 characteristic temperature of vibration (76)蒸气压下降 depression of vapor pressure (76)正常沸点 normal point (76)正吸附 positive adsorption (76)支链反应 branched chain reactions (76)直链反应 straight chain reactions (77)指前因子 pre-exponential factor (77)质量作用定律mass action law (77)制冷系数coefficient of refrigeration (77)中和热heat of neutralization (77)轴功 shaft work (77)转动配分函数 partition function of rotation (77)转动特征温度 characteristic temperature of vibration (78)转化率 convert ratio (78)转化温度conversion temperature (78)状态 state (78)状态方程 state equation (78)状态分布 state distribution (78)状态函数 state function (78)准静态过程quasi-static process (78)准一级反应 pseudo first order reaction (78)自动催化作用 auto-catalysis (78)自由度 degree of freedom (78)自由度数 number of degree of freedom (79)自由焓free enthalpy (79)自由能free energy (79)自由膨胀free expansion (79)组分数 component number (79)最低恒沸点 lower azeotropic point (79)最高恒沸点 upper azeotropic point (79)最佳反应温度 optimal reaction temperature (79)最可几分布 most probable distribution (80)最可几速率 most propable speed (80)概念及术语BET公式BET formula1938年布鲁瑙尔(Brunauer)、埃米特(Emmett)和特勒(Teller)三人在兰格缪尔单分子层吸附理论的基础上提出多分子层吸附理论。
统计学常用英语词汇
统计学常用英语词汇Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additive Noise, 加性噪声Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alpha factoring,α因子法Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis Of Effects, 效应分析Analysis Of Variance, 方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型ANOVA table and eta, 分组计算方差分析Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area 区域图Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
品质英语
品质英语A-DAbsolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法E-LEffect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量。
AnInquiryintoHis...
Cliodynamics: the Journal of Theoretical and Mathematical HistoryCorresponding author’s e-mail: ********************Citation: Krakauer, David C., John Gaddis, and Kenneth Pomeranz. 2011. Editors’Column: An Inquiry into History, Big History and Metahistory. Cliodynamics 2: 1–5.Editors’ Column: An Inquiry into History, Big History, and MetahistoryDavid C. Krakauer Santa Fe InstituteJohn Gaddis Yale UniversityKenneth PomeranzUniversity of California at IrvineWhat is history anyway? Most people would say it’s what happened in the past, but how far back does the past extend? To the first written sources? To what other forms of evidence reveal about pre-literate civilizations? What does that term mean – an empire, a nation, a city, a village, a family, a lonely hermit somewhere? Why stop with people: shouldn’t history also comprise the environment in which they exist, and if so on what scale and how far back? And as long as we’re headed in that direction, why stop with the earth and the solar system? Why not go all the way back to the Big Bang itself? There’s obviously no consensus on how to answer these questions, but even asking them raises another set of questions about history: who should be doing it? Traditionally trained historians, for whom archives are the only significant source? Historians willing to go beyond archives, who must therefore rely on, and to some extent themselves become, psychologists, sociologists, anthropologists, archeologists? But if they’re also going to take environments into account, don’t they also have to know something about climatology, biology, paleontology, geology, and even astronomy? And how can they do that without knowing some basic physics, chemistry, and mathematics?You see where this is going: history, by this capacious definition, includes everything that has happened up until the present moment – and because the present moment has already become the past by the time you’ve finished reading this sentence, history must also provide a basis (what other one could there be?) for anticipating the future.What is to prevent history, then, from being the study of “life, the universe, and everything,” as the late Douglas Adams proposed in his The Hitchhiker’s Guide to the Galaxy ? Nothing in principle, but there is a problem in practice, which is that no one person, or academic department, or professional discipline, or method of inquiry, can do it all. Students of this kind of Very BigKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 12History have for very good reasons divided themselves into fields, sub-fields, and even micro-fields, knowing that things rarely get simpler the more closely you look at them.Much good has come of this. Our knowledge of this capaciously defined past has expanded exponentially over the past several hundred years. We now have a much clearer sense of who we are and where we came from than was available, say, to Copernicus, when he first ventured the suggestion that the universe did not revolve around us.Some bad has come of this process as well, however. For if the volume of information in relation to time looks like a hockey stick as it approaches our era, rapidly accelerating in the production of contemporary knowledge –then it is a laminated hockey stick, the parts of which define a trajectory without interacting with one another. How much do we really know, therefore, about where we came from, who we are – and where we may be going – if the disciplines we’ve divided ourselves into have lost the languages that would allow them to speak to anyone apart from themselves?Moreover, it seems likely that the disciplines themselves develop less than optimally when they lack ready access to each other’s insights and methods. Indeed it seems likely that history suffers most of all from such segmentation. At least to some extent history, more than the study of literature, or economics, or political science (though perhaps not much more than anthropology or sociology) aims to integrate the understanding of how human social arrangements, technologies, interactions with the larger biosphere, intellectual creations, and even our habitual cognitive and emotional responses to the world around us have changed over a given period of time: no matter what s/he emphasizes as a researcher, the person who teaches a history of 19th century England knows it cannot omit dramatic changes in birth and death rates, the expansion of suffrage, the publication of The Origin of Species , the expansion of overseas possessions, or the environmental consequences of industrialization. So despite what has sometimes seemed a strong allergy to “theory” (of various sorts) in history departments, historians may have the most to gain by opening more lines of communication to people studying change over time in various phenomena and on various time-scales. These papers have grown out of a series of conversations and meetings, sponsored by the Santa Fe Institute, on how we might recover such languages. It proceeds from the proposition that if generalization is necessary within particular disciplines – how could it not be? – then it should also be useful across all the disciplines that take, as the subject of their inquiries, Very Big History. It pursues the possibility of taking what one of our contributors, Murray Gell-Mann, has called “a crude look at the whole.” It explores the possibility that the sciences of complexity and its many tributary fields and concepts pioneered at Santa Fe, may provide new methods, or minimallyKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 13metaphors, by which to do this. It is premised on the notion that curiosity – the foundation of all knowledge – requires the ability to be both a specialist and a generalist at the same time. And that this simultaneity of perspective is in need of new trans-disciplinary approaches and ideas. Our title History, Big History and Metahistory , requires a brief explanation. By “history,” we mean the study, chiefly, of written records, extending from the most ancient cuneiform tablet through the most recent e-mails and twitters. By “big history,” we mean all reconstructions of the past that do not rely on written materials. By “metahistory,” we mean the patterns that emerge from both modes of inquiry that make generalization, and hence analysis, possible. We do not mean to imply by this sequence of terms that moving to the method and scale of “big history” is the only way to search for meaningful patterns. We are, however, confident that juxtaposing types of inquiry developed to deal with change in literate societies and those developed to deal with a much longer record of change has proved to be one very useful way of exposing important, often neglected questions, both about what it makes sense to look for in the always incomplete records of the past and about how to do the looking. As in any good discussion, our contributors do not all agree with one another. Some insist that there are unifying principles, or laws, to which both human and biological history are subject. Others seek ideas, tools, and perhaps standards of truth from dynamical systems, evolution, and statistics that could augment traditional approaches to history, but do not necessarily see such borrowings as requiring that history and big history become a single discipline. One contributor sees any attempt at unification in the humanities as dangerous, and citing as precedents the extent to which social Darwinism was used to abuse less powerful people and societies. All do share the view, however, that history is too important – and too encompassing – to be analyzed exclusively through the methods of qualitative text-based narratives. We have arranged our contributors alphabetically, for no better reason than to shuffle their ideas and to avoid enforcing on this journal’s readers the editor’s conclusions.We start with David Christian who discusses the chronometric revolution, and how this has lead to a single historical continuum stretching all the way back to the big bang, allowing for what he calls, Grand Unified Stories.Douglas Erwin explores how paleontologists deal with the vagaries of preservation, and how statistical techniques developed in biology, have been applied to textual evidence, and the complexities of non-uniform trends leading to convergent and parallel events. John Gaddis shows that several 19th century searches for a science of history – those of Leo Tolstoy, Carl von Clausewitz, and Henry Adams –Krakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 14grasped key concepts of complexity theory, but lacked the means of visualizing and verifying it that are available today. Murray Gell-Mann discusses the nature of empirical regularities, and their relationship to measures of complexity. Gell-Mann illustrates how apparently complex histories and patterns can sometimes be organized using simple models of growth and scaling. Geoffrey Harpham discusses the possible limitations and abuses of unified frameworks of explanation, using the history of philology as a case study. Unchecked, scientific trajectories in a social matrix can lead to unjustified inferences. David Krakauer introduces a range of concepts from non-linear dynamics, statistical physics and evolutionary biology, that he argues should be of use to all students of history. Using examples from traditional historicism, Krakauer shows how history often uses analogs of concepts and tools expressed quantitatively in the natural sciences. John McNeill explores parallels between cultural and biological evolution, exploring patterns of increasing cultural heterogeneity through time, and the role that specialist (pandas) and generalist (pigs) societies and states have played in explaining these patterns.Ken Pomeranz describes the ways in which naming historical phenomena influences how we then analyze them. Arguing that many of the classification schemes that are conventional among historians serve some other purposes well, but are not very conducive to seeking meaningful generalizations or engaging in dialogue with scientists, he suggests other approaches, while also giving reasons why they are far more likely to complement than displace currently popular taxonomies. Fred Spier, speaking as an historian, explores how big history might be brought within a reductive framework of physics, using the concept of free energy rate density, as a means of organizing major transitions, from the abiotic to the biotic and cultural domains. Peter Turchin explores the value of general quantitative theory in areas where prediction is limited, and comparative data and retrodiction need to be explored. The transformation of natural history into quantitative biology is used as possible precedent and model for a transformation of qualitative history.Geerat Vermeij considers a grand, economic theory of history, in which biology and culture might both be subsumed. Concepts of competition, feedback and power provide potential unifying historical concepts.Geoffrey West argues for quantitative approaches to history through a suitable choice of coarse-grained variables. West argues that is unlikely that we shall discern common patterns at the level of individuals, but if we allowKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 15 ourselves to study collective phenomena, such as urban systems, then we might make surprising new discoveries. No reader is likely to find all of these contributions persuasive, or perhaps even congenial. Nonetheless, we think that most will gain more from engaging with them in their current diversity than they would gain from any superficial consensus we could wring from them. Readers may think of some papers as introducing them to new tools, potentially useful for their current inquiries or for others they had previously deemed impossible. Others stand as arguments about what sorts of inquiries should be attempted; still others as preliminary reports from lines of inquiry (in various historical disciplines) that it would be good for a wider range of scholars to know about. Each of these, of course, bears on the others, at least indirectly: what we should ask, what tools we have for answering new and old questions, and what people have found by asking unusual questions or using unusual tools are obviously overlapping issues. The overlaps on display here are not nearly large enough to let us suggest a single, unified agenda for further work; they are, however, sufficiently numerous to suggest many places where more focused inter-disciplinary projects might take root and prove fruitful. Perhaps even more important, these efforts should give readers what the meetings they sprang from gave to its participants: a better sense of the range of conversations we might join, the opportunities and problems in those discussions, and some ways in which joining new conversations will give us new ways of analyzing our common past.。
机械英语考试试题及答案
机械英语考试试题及答案一、选择题(每题2分,共20分)1. The term "mechanical engineering" refers to:A. The study of machinesB. The design and manufacture of mechanical systemsC. The operation of machineryD. The maintenance of mechanical equipment答案:B2. What is the function of a bearing in a mechanical system?A. To reduce frictionB. To increase efficiencyC. To provide powerD. To transmit motion答案:A3. The process of converting thermal energy into mechanical energy is known as:A. ElectrificationB. CombustionC. ThermodynamicsD. Hydrodynamics答案:C4. In mechanical design, the principle of "KISS" stands for:A. Keep It Simple, StupidB. Keep It Short and SimpleC. Keep It Simple and SafeD. Keep It Simple, Smart答案:A5. A gear train is used to:A. Change the direction of motionB. Increase the speed of rotationC. Decrease the speed of rotationD. All of the above答案:D6. What does CAD stand for in mechanical engineering?A. Computer-Aided DesignB. Computer-Aided DraftingC. Computer-Aided DevelopmentD. Computer-Aided Diagnostics答案:A7. The SI unit for force is:A. NewtonB. JouleC. PascalD. Watt答案:A8. What is the purpose of a flywheel in a mechanical system?A. To store energyB. To increase speedC. To reduce noiseD. To dissipate heat答案:A9. The term "hydraulics" is associated with the study of:A. Fluid dynamicsB. Solid mechanicsC. Structural analysisD. Thermal engineering答案:A10. The process of cutting a material to a specific shape is known as:A. MachiningB. CastingC. ForgingD. Extrusion答案:A二、填空题(每空1分,共10分)11. The formula for calculating the moment of a force is \( F \times d \), where \( F \) is the force and \( d \) is the_______.答案:distance from the pivot12. A _______ is a device that converts linear motion into rotational motion.答案:crank13. In a four-stroke internal combustion engine, the four strokes are intake, compression, _______, and exhaust.答案:power14. The _______ of a material is its ability to resist deformation under load.答案:stiffness15. The term "overhaul" in mechanical maintenance refers to a thorough inspection and _______ of a machine or its parts.答案:repair16. The _______ of a machine is the study of how forces act on and within a body.答案: statics17. A _______ is a type of machine that uses a screw to convert rotational motion into linear motion.答案:screw jack18. The _______ of a system is the point around which the system rotates.答案:pivot19. The _______ of a lever is the ratio of the effort arm to the load arm.答案:mechanical advantage20. The _______ is a type of bearing that allows for rotation with minimal friction.答案:ball bearing三、简答题(每题5分,共30分)21. Explain the difference between static and dynamic equilibrium in mechanical systems.答案:Static equilibrium refers to a state where the net force and net moment acting on a body are zero, resulting in no acceleration. Dynamic equilibrium occurs when the net force is zero, but the body is in motion with constant velocity.22. What is the purpose of a clutch in a vehicle?答案:A clutch is used to engage and disengage the power transmission from the engine to the transmission system, allowing the vehicle to start, stop, and change gears smoothly.23. Describe the function of a governor in an engine.答案:A governor is a device that automatically controls the speed of an engine by regulating the fuel supply or the valve settings, ensuring the engine operates within safespeed limits.24. What are the three primary types of joints in structural engineering?答案:The three primary types of joints are pinned joints, fixed joints, and sliding joints, each serving different purposes in connecting and supporting structural elements.25. Explain the。
薛定谔—麦克斯韦尔方程径向解的存在性和多重性(英文)
In 1887, the German physicist Erwin Schrödinger proposed a radial solution to the Maxwell-Schrödinger equation. This equation describes the behavior of an electron in an atom and is used to calculate its energy levels. The radial solution was found to be valid for all values of angular momentum quantum number l, which means that it can describe any type of atomic orbital.The existence and multiplicity of this radial solution has been studied extensively since then. It has been shown that there are infinitely many solutions for each value of l, with each one corresponding to a different energy level. Furthermore, these solutions can be divided into two categories: bound states and scattering states. Bound states have negative energies and correspond to electrons that are trapped within the atom; scattering states have positive energies and correspond to electrons that escape from the atom after being excited by external radiation or collisions with other particles.The existence and multiplicity of these solutions is important because they provide insight into how atoms interact with their environment through electromagnetic radiation or collisions with other particles. They also help us understand why certain elements form molecules when combined together, as well as why some elements remain stable while others decay over time due to radioactive processes such as alpha decay or beta decay.。
可靠性专业英语
可靠性工程质量专业英语词汇集Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOV A (analysis of variance), 方差分析ANOV A Models, 方差分析模型Arcing, 弧弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图箱尾图Breakdown bound, 崩溃界崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量依变量因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法Duncan新法E-LEffect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值极值F distribution, F分布F test, F检验Factor, 因素因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验多样本的秩和检验H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量M-RMain effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOV A , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩协方差Profile trace, 截面迹图Proportion, 比构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表S-ZSample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度量表Scatter diagram, 散点图Schematic plot, 示意图简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误α错误Type II error, 二类错误β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性V ARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转V olume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法配对符号秩和检验Wild point, 野点狂点Wild value, 野值狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
From Data Mining to Knowledge Discovery in Databases
s Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media atten-tion of late. What is all the excitement about?This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges in-volved in real-world applications of knowledge discovery, and current and future research direc-tions in the field.A cross a wide variety of fields, data arebeing collected and accumulated at adramatic pace. There is an urgent need for a new generation of computational theo-ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD).At an abstract level, the KDD field is con-cerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easi-ly) into other forms that might be more com-pact (for example, a short report), more ab-stract (for example, a descriptive approximation or model of the process that generated the data), or more useful (for exam-ple, a predictive model for estimating the val-ue of future cases). At the core of the process is the application of specific data-mining meth-ods for pattern discovery and extraction.1This article begins by discussing the histori-cal context of KDD and data mining and theirintersection with other related fields. A briefsummary of recent KDD real-world applica-tions is provided. Definitions of KDD and da-ta mining are provided, and the general mul-tistep KDD process is outlined. This multistepprocess has the application of data-mining al-gorithms as one particular step in the process.The data-mining step is discussed in more de-tail in the context of specific data-mining al-gorithms and their application. Real-worldpractical application issues are also outlined.Finally, the article enumerates challenges forfuture research and development and in par-ticular discusses potential opportunities for AItechnology in KDD systems.Why Do We Need KDD?The traditional method of turning data intoknowledge relies on manual analysis and in-terpretation. For example, in the health-careindustry, it is common for specialists to peri-odically analyze current trends and changesin health-care data, say, on a quarterly basis.The specialists then provide a report detailingthe analysis to the sponsoring health-care or-ganization; this report becomes the basis forfuture decision making and planning forhealth-care management. In a totally differ-ent type of application, planetary geologistssift through remotely sensed images of plan-ets and asteroids, carefully locating and cata-loging such geologic objects of interest as im-pact craters. Be it science, marketing, finance,health care, retail, or any other field, the clas-sical approach to data analysis relies funda-mentally on one or more analysts becomingArticlesFALL 1996 37From Data Mining to Knowledge Discovery inDatabasesUsama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00areas is astronomy. Here, a notable success was achieved by SKICAT ,a system used by as-tronomers to perform image analysis,classification, and cataloging of sky objects from sky-survey images (Fayyad, Djorgovski,and Weir 1996). In its first application, the system was used to process the 3 terabytes (1012bytes) of image data resulting from the Second Palomar Observatory Sky Survey,where it is estimated that on the order of 109sky objects are detectable. SKICAT can outper-form humans and traditional computational techniques in classifying faint sky objects. See Fayyad, Haussler, and Stolorz (1996) for a sur-vey of scientific applications.In business, main KDD application areas includes marketing, finance (especially in-vestment), fraud detection, manufacturing,telecommunications, and Internet agents.Marketing:In marketing, the primary ap-plication is database marketing systems,which analyze customer databases to identify different customer groups and forecast their behavior. Business Week (Berry 1994) estimat-ed that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for ex-ample, American Express reports a 10- to 15-percent increase in credit-card use. Another notable marketing application is market-bas-ket analysis (Agrawal et al. 1996) systems,which find patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.Investment: Numerous companies use da-ta mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million;since its start in 1993, the system has outper-formed the broad stock market (Hall, Mani,and Barr 1996).Fraud detection: HNC Falcon and Nestor PRISM systems are used for monitoring credit-card fraud, watching over millions of ac-counts. The FAIS system (Senator et al. 1995),from the U.S. Treasury Financial Crimes En-forcement Network, is used to identify finan-cial transactions that might indicate money-laundering activity.Manufacturing: The CASSIOPEE trou-bleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major Euro-pean airlines to diagnose and predict prob-lems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European first prize for innova-intimately familiar with the data and serving as an interface between the data and the users and products.For these (and many other) applications,this form of manual probing of a data set is slow, expensive, and highly subjective. In fact, as data volumes grow dramatically, this type of manual data analysis is becoming completely impractical in many domains.Databases are increasing in size in two ways:(1) the number N of records or objects in the database and (2) the number d of fields or at-tributes to an object. Databases containing on the order of N = 109objects are becoming in-creasingly common, for example, in the as-tronomical sciences. Similarly, the number of fields d can easily be on the order of 102or even 103, for example, in medical diagnostic applications. Who could be expected to di-gest millions of records, each having tens or hundreds of fields? We believe that this job is certainly not one for humans; hence, analysis work needs to be automated, at least partially.The need to scale up human analysis capa-bilities to handling the large number of bytes that we can collect is both economic and sci-entific. Businesses use data to gain competi-tive advantage, increase efficiency, and pro-vide more valuable services to customers.Data we capture about our environment are the basic evidence we use to build theories and models of the universe we live in. Be-cause computers have enabled humans to gather more data than we can digest, it is on-ly natural to turn to computational tech-niques to help us unearth meaningful pat-terns and structures from the massive volumes of data. Hence, KDD is an attempt to address a problem that the digital informa-tion era made a fact of life for all of us: data overload.Data Mining and Knowledge Discovery in the Real WorldA large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week , Newsweek , Byte , PC Week , and other large-circulation periodicals. Unfortu-nately, it is not always easy to separate fact from media hype. Nonetheless, several well-documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.In science, one of the primary applicationThere is an urgent need for a new generation of computation-al theories and tools toassist humans in extractinguseful information (knowledge)from the rapidly growing volumes ofdigital data.Articles38AI MAGAZINEtive applications (Manago and Auriol 1996).Telecommunications: The telecommuni-cations alarm-sequence analyzer (TASA) wasbuilt in cooperation with a manufacturer oftelecommunications equipment and threetelephone networks (Mannila, Toivonen, andVerkamo 1995). The system uses a novelframework for locating frequently occurringalarm episodes from the alarm stream andpresenting them as rules. Large sets of discov-ered rules can be explored with flexible infor-mation-retrieval tools supporting interactivityand iteration. In this way, TASA offers pruning,grouping, and ordering tools to refine the re-sults of a basic brute-force search for rules.Data cleaning: The MERGE-PURGE systemwas applied to the identification of duplicatewelfare claims (Hernandez and Stolfo 1995).It was used successfully on data from the Wel-fare Department of the State of Washington.In other areas, a well-publicized system isIBM’s ADVANCED SCOUT,a specialized data-min-ing system that helps National Basketball As-sociation (NBA) coaches organize and inter-pret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Su-personics, which reached the NBA finals.Finally, a novel and increasingly importanttype of discovery is one based on the use of in-telligent agents to navigate through an infor-mation-rich environment. Although the ideaof active triggers has long been analyzed in thedatabase field, really successful applications ofthis idea appeared only with the advent of theInternet. These systems ask the user to specifya profile of interest and search for related in-formation among a wide variety of public-do-main and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like (<http:// www.ffl/>). CRAYON(/>) allows users to create their own free newspaper (supported by ads); NEWSHOUND(<http://www. /hound/>) from the San Jose Mercury News and FARCAST(</> automatically search information from a wide variety of sources, including newspapers and wire services, and e-mail rele-vant documents directly to the user.These are just a few of the numerous suchsystems that use KDD techniques to automat-ically produce useful information from largemasses of raw data. See Piatetsky-Shapiro etal. (1996) for an overview of issues in devel-oping industrial KDD applications.Data Mining and KDDHistorically, the notion of finding useful pat-terns in data has been given a variety ofnames, including data mining, knowledge ex-traction, information discovery, informationharvesting, data archaeology, and data patternprocessing. The term data mining has mostlybeen used by statisticians, data analysts, andthe management information systems (MIS)communities. It has also gained popularity inthe database field. The phrase knowledge dis-covery in databases was coined at the first KDDworkshop in 1989 (Piatetsky-Shapiro 1991) toemphasize that knowledge is the end productof a data-driven discovery. It has been popular-ized in the AI and machine-learning fields.In our view, KDD refers to the overall pro-cess of discovering useful knowledge from da-ta, and data mining refers to a particular stepin this process. Data mining is the applicationof specific algorithms for extracting patternsfrom data. The distinction between the KDDprocess and the data-mining step (within theprocess) is a central point of this article. Theadditional steps in the KDD process, such asdata preparation, data selection, data cleaning,incorporation of appropriate prior knowledge,and proper interpretation of the results ofmining, are essential to ensure that usefulknowledge is derived from the data. Blind ap-plication of data-mining methods (rightly crit-icized as data dredging in the statistical litera-ture) can be a dangerous activity, easilyleading to the discovery of meaningless andinvalid patterns.The Interdisciplinary Nature of KDDKDD has evolved, and continues to evolve,from the intersection of research fields such asmachine learning, pattern recognition,databases, statistics, AI, knowledge acquisitionfor expert systems, data visualization, andhigh-performance computing. The unifyinggoal is extracting high-level knowledge fromlow-level data in the context of large data sets.The data-mining component of KDD cur-rently relies heavily on known techniquesfrom machine learning, pattern recognition,and statistics to find patterns from data in thedata-mining step of the KDD process. A natu-ral question is, How is KDD different from pat-tern recognition or machine learning (and re-lated fields)? The answer is that these fieldsprovide some of the data-mining methodsthat are used in the data-mining step of theKDD process. KDD focuses on the overall pro-cess of knowledge discovery from data, includ-ing how the data are stored and accessed, howalgorithms can be scaled to massive data setsThe basicproblemaddressed bythe KDDprocess isone ofmappinglow-leveldata intoother formsthat might bemorecompact,moreabstract,or moreuseful.ArticlesFALL 1996 39A driving force behind KDD is the database field (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot fit in the main memory is of fun-damental importance to KDD. Database tech-niques for gaining efficient data access,grouping and ordering operations when ac-cessing data, and optimizing queries consti-tute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memo-ry and pay no attention to how the algorithm breaks down if only limited views of the data are possible.A related field evolving from databases is data warehousing,which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways: (1) data cleaning and (2)data access.Data cleaning: As organizations are forced to think about a unified logical view of the wide variety of data and databases they pos-sess, they have to address the issues of map-ping data to a single naming convention,uniformly representing and handling missing data, and handling noise and errors when possible.Data access: Uniform and well-defined methods must be created for accessing the da-ta and providing access paths to data that were historically difficult to get to (for exam-ple, stored offline).Once organizations and individuals have solved the problem of how to store and ac-cess their data, the natural next step is the question, What else do we do with all the da-ta? This is where opportunities for KDD natu-rally arise.A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles pro-posed by Codd (1993). OLAP tools focus on providing multidimensional data analysis,which is superior to SQL in computing sum-maries and breakdowns along many dimen-sions. OLAP tools are targeted toward simpli-fying and supporting interactive data analysis,but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.Basic DefinitionsKDD is the nontrivial process of identifying valid, novel, potentially useful, and ultimate-and still run efficiently, how results can be in-terpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported. The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other fields of AI (be-sides machine learning) to contribute to KDD. KDD places a special emphasis on find-ing understandable patterns that can be inter-preted as useful or interesting knowledge.Thus, for example, neural networks, although a powerful modeling tool, are relatively difficult to understand compared to decision trees. KDD also emphasizes scaling and ro-bustness properties of modeling algorithms for large noisy data sets.Related AI research fields include machine discovery, which targets the discovery of em-pirical laws from observation and experimen-tation (Shrager and Langley 1990) (see Kloes-gen and Zytkow [1996] for a glossary of terms common to KDD and machine discovery),and causal modeling for the inference of causal models from data (Spirtes, Glymour,and Scheines 1993). Statistics in particular has much in common with KDD (see Elder and Pregibon [1996] and Glymour et al.[1996] for a more detailed discussion of this synergy). Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quan-tifying the uncertainty that results when one tries to infer general patterns from a particu-lar sample of an overall population. As men-tioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were first introduced. The concern arose because if one searches long enough in any data set (even randomly generated data),one can find patterns that appear to be statis-tically significant but, in fact, are not. Clearly,this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct rele-vance to KDD. Thus, data mining is a legiti-mate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical as-pects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree pos-sible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.Data mining is a step in the KDD process that consists of ap-plying data analysis and discovery al-gorithms that produce a par-ticular enu-meration ofpatterns (or models)over the data.Articles40AI MAGAZINEly understandable patterns in data (Fayyad, Piatetsky-Shapiro, and Smyth 1996).Here, data are a set of facts (for example, cases in a database), and pattern is an expres-sion in some language describing a subset of the data or a model applicable to the subset. Hence, in our usage here, extracting a pattern also designates fitting a model to data; find-ing structure from data; or, in general, mak-ing any high-level description of a set of data. The term process implies that KDD comprises many steps, which involve data preparation, search for patterns, knowledge evaluation, and refinement, all repeated in multiple itera-tions. By nontrivial, we mean that some search or inference is involved; that is, it is not a straightforward computation of predefined quantities like computing the av-erage value of a set of numbers.The discovered patterns should be valid on new data with some degree of certainty. We also want patterns to be novel (at least to the system and preferably to the user) and poten-tially useful, that is, lead to some benefit to the user or task. Finally, the patterns should be understandable, if not immediately then after some postprocessing.The previous discussion implies that we can define quantitative measures for evaluating extracted patterns. In many cases, it is possi-ble to define measures of certainty (for exam-ple, estimated prediction accuracy on new data) or utility (for example, gain, perhaps indollars saved because of better predictions orspeedup in response time of a system). No-tions such as novelty and understandabilityare much more subjective. In certain contexts,understandability can be estimated by sim-plicity (for example, the number of bits to de-scribe a pattern). An important notion, calledinterestingness(for example, see Silberschatzand Tuzhilin [1995] and Piatetsky-Shapiro andMatheus [1994]), is usually taken as an overallmeasure of pattern value, combining validity,novelty, usefulness, and simplicity. Interest-ingness functions can be defined explicitly orcan be manifested implicitly through an or-dering placed by the KDD system on the dis-covered patterns or models.Given these notions, we can consider apattern to be knowledge if it exceeds some in-terestingness threshold, which is by nomeans an attempt to define knowledge in thephilosophical or even the popular view. As amatter of fact, knowledge in this definition ispurely user oriented and domain specific andis determined by whatever functions andthresholds the user chooses.Data mining is a step in the KDD processthat consists of applying data analysis anddiscovery algorithms that, under acceptablecomputational efficiency limitations, pro-duce a particular enumeration of patterns (ormodels) over the data. Note that the space ofArticlesFALL 1996 41Figure 1. An Overview of the Steps That Compose the KDD Process.methods, the effective number of variables under consideration can be reduced, or in-variant representations for the data can be found.Fifth is matching the goals of the KDD pro-cess (step 1) to a particular data-mining method. For example, summarization, clas-sification, regression, clustering, and so on,are described later as well as in Fayyad, Piatet-sky-Shapiro, and Smyth (1996).Sixth is exploratory analysis and model and hypothesis selection: choosing the data-mining algorithm(s) and selecting method(s)to be used for searching for data patterns.This process includes deciding which models and parameters might be appropriate (for ex-ample, models of categorical data are differ-ent than models of vectors over the reals) and matching a particular data-mining method with the overall criteria of the KDD process (for example, the end user might be more in-terested in understanding the model than its predictive capabilities).Seventh is data mining: searching for pat-terns of interest in a particular representa-tional form or a set of such representations,including classification rules or trees, regres-sion, and clustering. The user can significant-ly aid the data-mining method by correctly performing the preceding steps.Eighth is interpreting mined patterns, pos-sibly returning to any of steps 1 through 7 for further iteration. This step can also involve visualization of the extracted patterns and models or visualization of the data given the extracted models.Ninth is acting on the discovered knowl-edge: using the knowledge directly, incorpo-rating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. This process also includes checking for and resolving po-tential conflicts with previously believed (or extracted) knowledge.The KDD process can involve significant iteration and can contain loops between any two steps. The basic flow of steps (al-though not the potential multitude of itera-tions and loops) is illustrated in figure 1.Most previous work on KDD has focused on step 7, the data mining. However, the other steps are as important (and probably more so) for the successful application of KDD in practice. Having defined the basic notions and introduced the KDD process, we now focus on the data-mining component,which has, by far, received the most atten-tion in the literature.patterns is often infinite, and the enumera-tion of patterns involves some form of search in this space. Practical computational constraints place severe limits on the sub-space that can be explored by a data-mining algorithm.The KDD process involves using the database along with any required selection,preprocessing, subsampling, and transforma-tions of it; applying data-mining methods (algorithms) to enumerate patterns from it;and evaluating the products of data mining to identify the subset of the enumerated pat-terns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which pat-terns are extracted and enumerated from da-ta. The overall KDD process (figure 1) in-cludes the evaluation and possible interpretation of the mined patterns to de-termine which patterns can be considered new knowledge. The KDD process also in-cludes all the additional steps described in the next section.The notion of an overall user-driven pro-cess is not unique to KDD: analogous propos-als have been put forward both in statistics (Hand 1994) and in machine learning (Brod-ley and Smyth 1996).The KDD ProcessThe KDD process is interactive and iterative,involving numerous steps with many deci-sions made by the user. Brachman and Anand (1996) give a practical view of the KDD pro-cess, emphasizing the interactive nature of the process. Here, we broadly outline some of its basic steps:First is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint.Second is creating a target data set: select-ing a data set, or focusing on a subset of vari-ables or data samples, on which discovery is to be performed.Third is data cleaning and preprocessing.Basic operations include removing noise if appropriate, collecting the necessary informa-tion to model or account for noise, deciding on strategies for handling missing data fields,and accounting for time-sequence informa-tion and known changes.Fourth is data reduction and projection:finding useful features to represent the data depending on the goal of the task. With di-mensionality reduction or transformationArticles42AI MAGAZINEThe Data-Mining Stepof the KDD ProcessThe data-mining component of the KDD pro-cess often involves repeated iterative applica-tion of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algo-rithms that incorporate these methods.The knowledge discovery goals are defined by the intended use of the system. We can distinguish two types of goals: (1) verification and (2) discovery. With verification,the sys-tem is limited to verifying the user’s hypothe-sis. With discovery,the system autonomously finds new patterns. We further subdivide the discovery goal into prediction,where the sys-tem finds patterns for predicting the future behavior of some entities, and description, where the system finds patterns for presenta-tion to a user in a human-understandableform. In this article, we are primarily con-cerned with discovery-oriented data mining.Data mining involves fitting models to, or determining patterns from, observed data. The fitted models play the role of inferred knowledge: Whether the models reflect useful or interesting knowledge is part of the over-all, interactive KDD process where subjective human judgment is typically required. Two primary mathematical formalisms are used in model fitting: (1) statistical and (2) logical. The statistical approach allows for nondeter-ministic effects in the model, whereas a logi-cal model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applica-tions given the typical presence of uncertain-ty in real-world data-generating processes.Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classification, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewilder-ing to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fun-damental techniques. The actual underlying model representation being used by a particu-lar method typically comes from a composi-tion of a small number of well-known op-tions: polynomials, splines, kernel and basis functions, threshold-Boolean functions, and so on. Thus, algorithms tend to differ primar-ily in the goodness-of-fit criterion used toevaluate model fit or in the search methodused to find a good fit.In our brief overview of data-mining meth-ods, we try in particular to convey the notionthat most (if not all) methods can be viewedas extensions or hybrids of a few basic tech-niques and principles. We first discuss the pri-mary methods of data mining and then showthat the data- mining methods can be viewedas consisting of three primary algorithmiccomponents: (1) model representation, (2)model evaluation, and (3) search. In the dis-cussion of KDD and data-mining methods,we use a simple example to make some of thenotions more concrete. Figure 2 shows a sim-ple two-dimensional artificial data set consist-ing of 23 cases. Each point on the graph rep-resents a person who has been given a loanby a particular bank at some time in the past.The horizontal axis represents the income ofthe person; the vertical axis represents the to-tal personal debt of the person (mortgage, carpayments, and so on). The data have beenclassified into two classes: (1) the x’s repre-sent persons who have defaulted on theirloans and (2) the o’s represent persons whoseloans are in good status with the bank. Thus,this simple artificial data set could represent ahistorical data set that can contain usefulknowledge from the point of view of thebank making the loans. Note that in actualKDD applications, there are typically manymore dimensions (as many as several hun-dreds) and many more data points (manythousands or even millions).ArticlesFALL 1996 43Figure 2. A Simple Data Set with Two Classes Used for Illustrative Purposes.。
SPSS术语中英文对照详解
【常用软件】SPSS术语中英文对照SPSS的统计分析过程均包含在Analysis菜单中。
我们只学以下两大分析过程:Descriptive Statistics(描述性统计)和Multiple Response(多选项分析)。
Descriptive Statistics(描述性统计)包含的分析功能:1.Frequencies 过程:主要用于统计指定变量各变量值的频次(Frequency)、百分比(Percent)。
2.Descriptives过程:主要用于计算指定变量的均值(Mean)、标准差(Std.Deviation)。
3.Crosstabs 过程:主要用于两个或两个以上变量的交叉分类。
Multiple Response(多选项分析)的分析功能:1.Define Set过程:该过程定义一个由多选项组成的多响应变量。
2.Frequencies过程:该过程对定义的多响应变量提供一个频数表。
3.Crosstabs过程:该过程提供所定义的多响应变量与其他变量的交叉分类表。
Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes‘ theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan‘s new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini‘s mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall‘s rank c orrelation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal和Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Pro, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh‘s test, 雷氏检验Rayleigh‘s Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden‘s index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
(完整)循证医学名词术语中英文对照索引
(完整)循证医学名词术语中英文对照索引循证医学名词术语中英文对照索引(以首字的汉语拼音为序)A安全性 SafetyB半随机对照试验 quasi— randomized control trial,qRCT背景问题 background questions比值比 odds ratio,OR标准化均数差 standardized mean difference, SMD病例报告 case report病例分析 case analysis病人价值观 patient value (此词删除)病人预期事件发生率 patient’s expected event rate, PEER补充替代医学 complementary and alternative medicine, CAM不良事件 adverse event不确定性 uncertaintyCCochrane图书馆 Cochrane Library, CL Cochrane系统评价 Cochrane systematic review, CSRCochrane协作网 Cochrane Collaboration, CC Cox比例风险模型 Cox’ proportional hazard model参考试验偏倚 References test bias肠激惹综合征 irritable bowel syndrome,IRB测量变异 measurement variation成本—效果 cost-effectiveness成本—效果分析 cost—effectiveness analysis成本—效益分析 cost-benefit analysis成本—效用分析 cost—utility analysis成本最小化分析(最小成本分析)cost-minimization analysis重复发表偏倚 Multiple publication bias 传统医学 Traditional Medicine,TMDD—L法 DerSimonian & Laird method发生一例不良反应所需治疗的病例数the number needed to harm one more patients from the therapy,NNH对抗疗法 allopathic medicine,AM对照组中某事件的发生率 control event rate,CER多重发表偏倚 multiple publication bias (删除此词)E二次研究 secondary studies二次研究证据 secondary research evidenceF发表偏倚 publication bias防止1例不良事件发生或得到1例有利结果需要治疗的病例数number needed to treat,NNT非随机同期对照试验 non—randomized concurrent control trial分层随机化 stratified randomization分类变量 categorical variable风险(危险度) riskG干扰 co-intervention工作偏倚 Workup bias固定效应模型 fixed effect model国际临床流行病学网 International Clinical Epidemiology Network, INCLENH灰色文献 grey literature后效评价 reevaluation获益 benefitJ(完整)循证医学名词术语中英文对照索引机会结 chance node疾病谱偏倚 Spectrum bias技术特性 Technical properties加权均数差 weighted mean difference, WMD 假阳性率(误诊率) false positive rate假阴性率(漏诊率) false negative rate简单随机化 simple randomization检索策略 search strategy交叉对照研究(交叉设计) crossover design 经济学分析 economic analysis经济学特性 Economic attributes or impacts经验医学 empirical medicine精确性 precision决策结 decision node决策树分析 decision tree analysis绝对获益增加率 absolute benefit increase, ABI 绝对危险度降低率 absolute risk reduction, ARR 绝对危险度增加率 absolute risk increase, ARIK可重复性 repeatability,reproducibility可靠性(信度) reliability可信区间 confidence interval ,CI可信限 confidence limit ,CLLLogistic回归模型 Logistic regression model历史性对照研究 historical control trial利弊比 likelihood of being helped vs harmed,LHH连续性变量 continuous variable临床对照试验 controlled clinical trial, CCT临床结局 clinical outcome临床经济学 clinical economics临床决策分析 clinical decision analysis临床流行病学 clinical epidemiology, CE临床实践指南 clinical practice guidelines, CPG 临床试验 clinical trial 临床研究证据 clinical research evidence临床证据 clinical evidence临床证据手册 handbook of clinical evidence零点 Zero time灵活性 flexibility临界点 Cut off points漏斗图 funnel plots率差(或危险差) rate difference,risk difference,RDMMeta—分析 Meta-analysis敏感度 sensitivity敏感性分析 sensitivity analysis墨克手册 Merck manualN脑卒中病房 Stroke Unit内在真实性 internal validityP偏倚 biasQ起始队列 inception cohort前—后对照研究 before-after study前景问题 foreground questions区组随机化 block randomizationS散点图 scatter plots森林图 forest plots伤残调整寿命年 disability adjusted life year,DALY生存曲线 survival curves生存时间 survival time生存质量(生活质量) quality of life世界卫生组织 World Health Organization, WHO 失安全数 fail—Safe Number试验组某事件发生率 experimental event rate,(完整)循证医学名词术语中英文对照索引EER似然比 likelihood Ratio, LR适用性 applicability受试者工作特征曲线(ROC曲线)receiver operator characteristic curve随机对照临床试验 randomized clinical trials, RCT随机对照试验 randomized control trial, RCT 随机化隐藏 randomization concealment随机效应模型 random effect modelT特异度 specificity同行评价 colleague evaluation统计效能(把握度) power同质性检验 tests for homogeneityW外在真实性 external validity完成治疗分析 per protocol,PP腕管综合征 carpal tunnel syndrome, CTS卫生技术 health technology卫生技术评估 health technology assessment,HTAX系统评价 systematic review, SR相对获益增加率 relative benefit increase, RBI 相对危险度 relative risk,RR相对危险度降低率 relative risk reduction, RRR 相对危险度增加率 relative risk increase, RRI 效果 effectiveness效力 efficacy效应尺度 effect magnitude效应量 effect size序贯试验 sequential trial选择性偏倚 selection bias循证儿科学 evidence-based pediatrics循证妇产科学 evidence—based gynecology & obstetrics循证购买 evidence-based purchasing循证护理 evidence-based nursing循证决策 evidence—based decision—making 循证内科学 evidence-based internal medicine 循证筛选 evidence—based selection循证外科学 evidence-based surgery循证卫生保健 evidence-based health care循证诊断 evidence-based diagnosis循证医学 evidence—based medicine, EBMY亚组分析 subgroup analysis严格评价 critical appraisal验后比 post—test odds验后概率 post-test probability验前比 pre-test odds验前概率 pre—test probability阳性预测值 positive predictive value原始研究 primary studies异质性检验 tests for heterogeneity意向治疗分析 intention-to—treat, ITT阴性预测值 negative predictive value引用偏倚 citation bias尤登指数 Youden’s index语言偏倚 language bias预后 prognosis预后因素 prognostic factor预后指数 prognostic index原始研究证据 primary research evidence原始研究证据来源 primary resourcesZ沾染 contamination真实性(效度) validity诊断参照标准 reference standard of diagnosis 诊断阈值 testing threshold诊断﹣治疗阈值 test—treatment threshold(完整)循证医学名词术语中英文对照索引质量调整寿命年 quality adjusted life year,QALY治疗阈值 Treatment threshold准确度 accuracy自我评价 self-evaluation最佳证据 best evidenceAbsolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals,绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension,加速度空间的维数Acceleration tangential,切向加速度Acceleration vector, 加速度向量Acceptable hypothesis,可接受假设Accumulation,累积Accuracy,准确度Actual frequency,实际频数Adaptive estimator,自适应估计量Addition,相加Addition theorem,加法定理Additivity,可加性Adjusted rate,调整率Adjusted value,校正值Admissible error,容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series,时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation,反正弦变换Area under the curve, 曲线面积AREG ,评估从一个时间点到下一个时间点回归相关时的误差ARIMA,季节和非季节性单变量模型的极大似然估计Arithmetic grid paper,算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit,拟合的评估Associative laws,结合律Asymmetric distribution,非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance,渐近方差Attributable risk,归因危险度Attribute data,属性资料Attribution,属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length,平均置信区间长度Average growth rate,平均增长率Bar chart, 条形图Bar graph,条形图Base period, 基期Bayes’ theorem , Bayes定理Bell—shaped curve,钟形曲线Bernoulli distribution,伯努力分布Best—trim estimator, 最好切尾估计量(完整)循证医学名词术语中英文对照索引Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval,双权区间Biweight M-estimator,双权M估计量Block,区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots,箱线图/箱尾图Breakdown bound,崩溃界/崩溃点Canonical correlation,典型相关Caption,纵标目Case-control study, 病例对照研究Categorical variable,分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and—effect relationship, 因果关系Cell, 单元Censoring,终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency,集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance,机遇Chance error, 随机误差Chance variable,随机变量Characteristic equation, 特征方程Characteristic root,特征根Characteristic vector,特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces,切尔诺夫脸谱图Chi-square test,卡方检验/χ2检验Choleskey decomposition,乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid—value,组中值Class upper limit, 组上限Classified variable,分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding,编码Coefficient of contingency,列联系数Coefficient of determination,决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production—moment correlation,积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness,偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect,列效应Column factor,列因素Combination pool,合并Combinative table,组合表Common factor,共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance,共性方差(完整)循证医学名词术语中英文对照索引Comparability,可比性Comparison of bathes, 批比较Comparison value,比较值Compartment model, 分部模型Compassion,伸缩Complement of an event,补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics,完备统计量Completely randomized design,完全随机化设计Composite event,联合事件Composite events,复合事件Concavity, 凹性Conditional expectation,条件期望Conditional likelihood,条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit,置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis ,验证性因子分析Confirmatory research, 证实性实验研究Confounding factor,混杂因素Conjoint,联合分析Consistency,相合性Consistency check,一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint,约束Contaminated distribution,污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution,污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control,对照Controlled experiments,对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean,校正均值Correction coefficient,校正系数Correctness,正确性Correlation coefficient,相关系数Correlation index, 相关指数Correspondence,对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant,共变Cox Regression, Cox回归Criteria for fitting,拟合准则Criteria of least squares,最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design,交叉设计Cross-section analysis,横断面分析Cross-section survey, 横断面调查Crosstabs ,交叉表Cross—tabulation table,复合表Cube root,立方根Cumulative distribution function,分布函数(完整)循证医学名词术语中英文对照索引Cumulative probability, 累计概率Curvature,曲率/弯曲Curvature,曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression,曲线回归Curvilinear relation,曲线关系Cut-and—try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank,数据库Data capacity,数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation,数据处理Data processing, 数据处理Data reduction, 数据缩减Data set,数据集Data sources,数据来源Data transformation,数据变换Data validity,数据有效性Data-in,数据输入Data-out, 数据输出Dead time,停滞期Degree of freedom, 自由度Degree of precision,精密度Degree of reliability,可靠性程度Degression,递减Density function, 密度函数Density of data points,数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix,导数矩阵Derivative—free methods,无导数方法Design,设计Determinacy,确定性Determinant,行列式Determinant, 决定因素Deviation,离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization,直接标准化法Discrete variable,离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function,判别值Dispersion,散布/分散度Disproportional, 不成比例的Disproportionate sub—class numbers,不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape,分布形状Distribution—free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve,剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution,双指数分布Double logarithmic,双对数Downward rank,降秩Dual-space plot,对偶空间图DUD,无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应(完整)循证医学名词术语中英文对照索引Eigenvalue,特征值Eigenvector, 特征向量Ellipse,椭圆Empirical distribution,经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun—class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error,误差/错误Error of estimate, 估计误差Error type I,第一类错误Error type II, 第二类错误Estimand,被估量Estimated error mean squares,估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance,欧式距离Event, 事件Event, 事件Exceptional data point,异常数据点Expectation plane,期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling,试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis,探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth,指数式增长EXSMOOTH, 指数平滑方法Extended fit,扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes,极端值/极值F distribution, F分布F test, F检验Factor,因素/因子Factor analysis,因子分析Factor Analysis,因子分析Factor score, 因子得分Factorial, 阶乘Factorial design,析因试验设计False negative,假阴性False negative error,假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate,病死率Field investigation,现场调查Field survey, 现场调查Finite population, 有限总体Finite—sample,有限样本First derivative, 一阶导数First principal component,第一主成分First quartile,第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve,曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table,四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon,频数多边图Frontier point,界限点(完整)循证医学名词术语中英文对照索引Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment,高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models),广义线性模型Geometric mean,几何平均数Gini's mean difference,基尼均差GLM (General liner models),通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant,行列式的梯度Graeco—Latin square, 希腊拉丁方Grand mean,总均值Gross errors, 重大错误Gross—error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half—life, 半衰期Hampel M-estimators,汉佩尔M估计量Happenstance,偶然事件Harmonic mean,调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy—tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity,不同质Heterogeneity of variance,方差不齐Hierarchical classification,组内分组Hierarchical clustering method, 系统聚类法High-leverage point,高杠杆率点HILOGLINEAR,***列联表的层次对数线性模型Hinge,折叶点Histogram,直方图Historical cohort study,历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M—estimators,休伯M估计量Hyperbola,双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence,独立性Independent variable,自变量Index,指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve,影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level,最初水平Interaction, 交互作用Interaction terms,交互作用项Intercept, 截距Interpolation,内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature,固有曲率Invariance, 不变性Inverse matrix,逆矩阵(完整)循证医学名词术语中英文对照索引Inverse probability, 逆概率Inverse sine transformation,反正弦变换Iteration,迭代Jacobian determinant,雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution,联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan—Merier chart, Kaplan—Merier图Kendall’s rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫—斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit,失拟Ladder of powers,幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage,泄漏Least favorable configuration,最不利构形Least favorable distribution,最不利分布Least significant difference,最小显著差法Least square method,最小二乘法Least-absolute—residuals estimates,最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least—absolute—residuals line, 最小绝对残差线Legend,图例L-estimator, L估计量L-estimator of location,位置L估计量L-estimator of scale,尺度L估计量Level, 水平Life expectance,预期期望寿命Life table,寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function,似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading,载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance,位置不变性Location scale family,位置尺度族Log rank test, 时序检验Logarithmic curve,对数曲线Logarithmic normal distribution,对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation,对数变换Logic check, 逻辑检查Logistic distribution,逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, ***列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation,低度相关Lower limit, 下限(完整)循证医学名词术语中英文对照索引Lowest—attained variance,最小可达方差LSD,最小显著差法的简称Lurking variable,潜在变量Main effect,主效应Major heading,主辞标目Marginal density function,边缘密度函数Marginal probability,边缘概率Marginal probability distribution,边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution,分布的匹配Matching of transformation, 变换的匹配Mathematical expectation,数学期望Mathematical model, 数学模型Maximum L—estimator,极大极小L 估计量Maximum likelihood method,最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means),均值—均值比较Median,中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish,中位数平滑Median test,中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator,最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers,离群值模型Modifying the model, 模型的修正Modulus of continuity,连续性模Morbidity,发病率Most favorable configuration,最有利构形Multidimensional Scaling (ASCAL), ***尺度/***标度Multinomial Logistic Regression ,多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions,多解Multiplication theorem,乘法定理Multiresponse,多元响应Multi—stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive,互不相容Mutual independence, 互相独立Natural boundary,自然边界Natural dead,自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable,名义变量Nonconstancy of variability,变异的非定常性Nonlinear regression,非线性相关Nonparametric statistics,非参数统计Nonparametric test,非参数检验Nonparametric tests,非参数检验(完整)循证医学名词术语中英文对照索引Normal deviate, 正态离差Normal distribution, 正态分布Normal equation,正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis,无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test,单侧检验One-way analysis of variance,单因素方差分析Oneway ANOVA ,单因素方差分析Open sequential trial, 开放型序贯设计Optrim,优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable,有序变量Orthogonal basis,正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN,正交设计Outlier cutoffs,离群值截断点Outliers,极端值OVERALS , 多组变量的非线性正规相关Overshoot,迭代过度Paired design,配对设计Paired sample,配对样本Pairwise slopes,成对斜率Parabola,抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics,参数统计Parametric test, 参数检验Partial correlation,偏相关Partial regression, 偏回归Partial sorting,偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling,退层Percent bar graph,百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation,排列P—estimator, P估计量Pie graph,饼图Pitman estimator, 皮特曼估计量Pivot,枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution,泊松分布Polishing,平滑Polled standard deviation,合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial,多项式Polynomial curve, 多项式曲线Population,总体Population attributable risk,人群归因危险度Positive correlation, 正相关Positively skewed,正偏Posterior distribution,后验分布Power of a test,检验效能(完整)循证医学名词术语中英文对照索引Precision,精密度Predicted value,预测值Preliminary analysis, 预备性分析Principal component analysis,主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model,概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling,按比例分层随机抽样Proportionate,成比例Proportionate sub—class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities,亲近性Pseudo F test,近似F检验Pseudo model, 近似模型Pseudosigma,伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification,属性分类Qualitative method,定性方法Quantile-quantile plot, 分位数-分位数图/Q—Q 图Quantitative analysis, 定量分析Quartile,四分位数Quick Cluster,快速聚类Radix sort,基数排序Random allocation,随机化分组Random blocks design, 随机区组设计Random event,随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data,等级资料Rate,比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test,雷氏检验Rayleigh's Z,雷氏Z值Reciprocal,倒数Reciprocal transformation,倒数变换Recording,记录Redescending estimators,回降估计量Reducing dimensions,降维Re-expression,重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square,回归平方和Rejection point,拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization,重新设置参数Replication,重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance,耐抗性Resistant line,耐抗线Resistant technique,耐抗技术R-estimator of location, 位置R估计量R—estimator of scale,尺度R估计量Retrospective study,回顾性调查(完整)循证医学名词术语中英文对照索引Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding,舍入Row,行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient,样本回归系数Sample size,样本量Sample standard deviation, 样本标准差Sampling error,抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram,散点图Schematic plot,示意图/简图Score test,计分检验Screening,筛检SEASON,季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper,半对数格纸Sensitivity curve,敏感度曲线Sequential analysis,贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method,贯序法Sequential test,贯序检验法Serial tests, 系列试验Short—cut method,简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test,符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling,简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression,简单回归simple table,简单表Sine estimator,正弦估计量Single—valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness,偏度Slash distribution,斜线分布Slope,斜率Smirnov test,斯米尔诺夫检验Source of variation,变异来源Spearman rank correlation,斯皮尔曼等级相关Specific factor,特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread,展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation,假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation,标准差Standard error,标准误Standard error of difference, 差别的标准误Standard error of estimate,标准估计误差Standard error of rate, 率的标准误(完整)循证医学名词术语中英文对照索引Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table,统计表Steepest descent,最速下降法Stem and leaf display,茎叶图Step factor,步长因子Stepwise regression,逐步回归Storage,存Strata, 层(复数)Stratified sampling,分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship,结构关系Studentized residual, 学生化残差/t化残差Sub—class numbers,次级组含量Subdividing,分割Sufficient statistic,充分统计量Sum of products,积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression,偏回归平方和Sure event, 必然事件Survey,调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error,系统误差Systematic sampling,系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line,切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses,假设检验Theoretical frequency,理论频数Time series, 时间序列Tolerance interval,容忍区间Tolerance lower limit,容忍下限Tolerance upper limit, 容忍上限Torsion,扰率Total sum of square, 总平方和Total variation, 总变异Transformation,转换Treatment,处理Trend,趋势Trend of percentage, 百分比趋势Trial,试验Trial and error method,试错法Tuning constant, 细调常数Two sided test,双向检验Two-stage least squares, 二阶最小平方Two-stage sampling,二阶段抽样Two—tailed test,双侧检验Two—way analysis of variance, 双因素方差分析Two—way table,双向表Type I error, 一类错误/α错误Type II error,二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate,无偏估计(完整)循证医学名词术语中英文对照索引Unconstrained nonlinear regression ,无约束非线性回归Unequal subclass number,不等次级组含量Ungrouped data,不分组资料Uniform coordinate,均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate,方差一致最小无偏估计Unit, 单元Unordered categories,无序分类Upper limit, 上限Upward rank, 升秩Vague concept,模糊概念Validity, 有效性VARCOMP (Variance component estimation),方差元素估计Variability, 变异性Variable,变量Variance, 方差Variation, 变异Varimax orthogonal rotation,方差最大正交旋转Volume of distribution,容积W test, W检验Weibull distribution,威布尔分布Weight,权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method,加权直线回归Weighted mean,加权平均数Weighted mean square,加权平均方差Weighted sum of square,加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location,位置W估计量Width,宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean,缩尾均值Withdraw,失访Youden's index, 尤登指数Z test, Z检验Zero correlation,零相关Z—transformation, Z变换安全性, Safety半随机对照试验, quasi— randomized control trial,qRCT背景问题, background questions比值比, odds ratio,OR标准化均数差, standardized mean difference,SMD病例报告, case report病例分析, case analysis病人价值观, patient value (此词删除)病人预期事件发生率, patient’s expected event rate, PEER补充替代医学, complementary and alternative medicine, CAM不良事件, adverse event不确定性, uncertaintyCochrane图书馆, Cochrane Library, CL Cochrane系统评价, Cochrane systematic review, CSRCochrane协作网, Cochrane Collaboration,CCCox比例风险模型, Cox’ proportional hazard model参考试验偏倚, References test bias肠激惹综合征, irritable bowel syndrome,IRB 测量变异, measurement variation。
Microsoft Azure 企业版说明书
Azure for EnterprisesWhat and Why?@DChappellAssocCopyright © 2015 Chappell & AssociatesMicrosoft AzureA public cloud platformMicrosoft Azure provides Internet-accessible computing resources‒It runs in data centers around the worldUSUSUSUSEuropeEurope AsiaAsia Asia Asia AustraliaBrazilSecurityCan a public cloud platform keep my data and applications safe?ANSWERYou must learn to trust your public cloud providerCan I still meet regulatoryrequirements in the public cloud?ComplianceLaws and Regulations for Off-Premises ComputingFINANCIAL SERVICESHEALTHCARERETAILINGNATIONAL GOVERNMENTLOCAL GOVERNMENT. . .C O U N T R YGermanyUnited StatesUnited KingdomFranceAustraliaSouth Korea. . .?????????????????????????????????????ANSWERYou must understand the rules that apply to youComputeWeb Sites, Cloud ServicesVM VMApplicationVirtual Machines VM VM ImagesVM Create and use virtual machinesProvide applicationsInfrastructure as a Service (IaaS)Platform as a Service (PaaS)SQL Database, …Data managementBlob Storage100011010011110111110110100011010011110111110110100011010011110111110110DBMS in a VMVMSQL Server,MySQL, …Binary storage IaaS relational storage PaaS relationalstorageMicrosoft Azure Pricing examples (in US dollars)User BandwidthInbound:FreeOutbound:$0.05 to $0.087/GBUS and Europe,$0.12 to $0.138/GBAsia/Pacific,$0.16 to $0.181/GBBrazilDataBlob Storage: $0.022 to $0.061/GB per month,depending on size and capabilitiesSQL Database: $5to $3,720 per month,depending on database size and throughputComputeVirtual Machines: $0.02 to $1.32/instance perhour, depending on instance size and capabilitiesEnterprise agreements, etc.commonly discount these pricesWhat Public Cloud Platforms Can ProvideInfrastructureInfrastructureExample scenarios▪Data storage▪Cloud identity▪VMs on demand▪Disaster recovery▪Deploying packaged applications▪Moving existing applications to the public cloudBlobs100011010011001111011111011011010001101100011010011001111011111011011010001101100011010011001111011111011011010001101100011010011001111011111011011010001101100011010011001111011111011011010001101100011010011001111011111011011010001101E N T E R P R I S EM I C R O S O F T A Z U R EData StorageExample: Using Azure BlobsSAN appliance for hybridstorageMicrosoft StorSimpleWindows Server, SQL Server, …Store backup dataApplicationsStore arbitrary binary data,e.g., videosData Storage Why do this?Lower costEXAMPLEOne terabyte stored ingeo-redundant blobs▪Operations on the data:10,000,000/month▪Data transfer out: 500gigabytes/month COSTSStorage: $61/monthOperations: $0.50/monthData transfer: $43.01/month (US/Europe)$68.31/month (Asia/Pacific)$89.60/month (Brazil)Total: $104.51/month (US/Europe)$129.81/month (Asia/Pacific)$151.10/month (Brazil)$Example: Single sign-on for SaaS applicationsE N T E R P R I S EM I C R O S O F T A Z U R E1LoginIT AdminWindows Server Active DirectoryUser2Configure linkAzure Active DirectoryO T H E R C L O U D E N V I R O N M E N TSaaS ApplicationSaaS Application3Access on-premises and SaaS applicationsOn-Premises ApplicationWhy do this?Single sign-on to diverse SaaS applicationsAzure AD Premium supports:-Office 365-Dynamics CRM Online-Google Apps-Salesforce CRM-ServiceNow-Dropbox-Many more Multi-factor authenticationAzure AD Premium canrequire a password plusphone-delivered code forloginsSimpler identityadministrationAzure AD Premium provides:-Self-service passwordresets for SaaS applications-Reports of who accessedwhich applications, etc.E N T E R P R I S EM I C R O S O F T A Z U R EExample: A dev/test environment on AzureVMsDevelopersMicrosoft Azure Virtual Machines1Create VMs2Use VMsIT Admin or DeveloperMicrosoft Azure Management PortalWhy do this?Fast and simple way to get inexpensive VMsCan use Microsoft Azure-provided VHDs or your own, Windows or LinuxUsers can potentially access cloud VMs as if they were local Useful in many situationsDev/test environment forcloud or on-premises appsInnovation/proof ofconcept projectsCan shut down VMs whenthey’re not neededSuch as nights or weekendswhen developers aren’t activeExample: Database failover to AzureM I C R O S O F T A Z U R EE N T E R P R I S EUsersVMSQL ServerSQL ServerApp2Create SQL ServerAlwaysOn availability group3Redirect here if an on-premises failure occursAzureVirtual Machines1Create VMIT AdminAzureManagement PortalWhy do this?Can cover a range of scenariosAnother option, Azure Site Recovery, allows replicating Hyper-V and VMware VMs in the cloudVMs can be grouped together, then started in a specific order Lower costNo need to maintain adedicated facility just forDRCan instead potentiallycreate (and pay for) VMsonly when they’re neededProvides global recoveryoptionsMicrosoft Azure hasdatacenters around the worldExample: SharePoint on AzureE N T E R P R I S EM I C R O S O F T A Z U R EIT AdminUsersAzureVirtual Machines1Create VMsVMsVMsSharePoint SQL2Deploy and configure SharePointAzureManagement PortalFaster deployment No need to wait for central IT IT resources become anoperating expenseRather than a capitalexpenseLower costMicrosoft Azure is probablycheaper today and certainlycheaper tomorrow; prices keepgoing downWhy do this?Moving Existing Applications to the Public CloudExample: Moving a custom application to AzureE N T E R P R I S EM I C R O S O F T A Z U R EIT AdminUsersAzureVirtual Machines1Create VMsVMsVMsApplication DBMS2Deploy and configure applicationAzureManagement PortalMoving Existing Applications to the Public Cloud Why do this?Lower costEXAMPLETwo medium VMs ($.18/houreach) running continuouslyStores 100 gigabytes▪Operations on the data:30,000,000/month▪Data transfer out: 50gigabytes/month COSTSCompute: $268.00/month Storage: $6.10/month Bandwidth: $3.92/month (US and Europe)$6.21/month (Asia/Pacific)$8.15/month (Brazil) Total: $278.02/month (US/Europe)$280.31/month (Asia/Pacific)$282.25/month (Brazil)$Making good decisions here requiresknowing your current costsInfrastructureSummarizing the scenarios▪Data storage▪Cloud identity▪VMs on demand▪Disaster recovery▪Deploying packaged applications▪Moving existing applications to the public cloudWhat Public Cloud Platforms Can ProvideApplicationsApplicationsExample scenarios▪New employee-facing applications ▪New customer-facing applications ▪New parallel applicationsExample: An IaaS applicationE N T E R P R I S EM I C R O S O F T A Z U R EDeveloperUsersAzureVirtual Machines1Create VMs2Deploy application and dataAzureManagement PortalVMVMApplication DBMSEase and speed of deploymentNo need to wait for central IT Capabilities you can’t easilyget otherwiseGeographic distributionEasy up-and-down scalingEspecially important forspiky appsLower costBecause of public cloud platformscale and/or elasticityWhy do this?Example: A PaaS applicationM I C R O S O F T A Z U R ECloud Services/ Web Sites1Deploy applicationSQL Database/ DocumentDB/TablesApplicationE N T E R P R I S EDeveloperUsersAzureManagement PortalEase and speed of deploymentThe PaaS platform already exists--no need to create it Lower management costThe PaaS platformmaintains theenvironment for youLower riskFewer things to configure meansfewer opportunities for errorNew Employee-Facing Application with PaaS Why use PaaS rather than IaaS?Example: A PaaS applicationE N T E R P R I S EM I C R O S O F T A Z U R EDeveloperMicrosoft Azure Management PortalMicrosoft Azure Cloud Services/ Web Sites1Deploy application and dataVMsVMs AppSQL DatabaseC U S T O M E R SCapabilities you can’t easily get otherwise, such as:Massive scaleEasy up-and-down scaling High reliability Geographic distribution NoSQL database service Lower costBecause of public cloud platform scale and elasticityWhy do this?Ease and speed ofdeploymentEspecially with PaaSNew Customer-Facing ApplicationsWhere public cloud platforms are an especially good fitExamplesCloud backends for enterprisemobile applications Online ticket sales Marketing web sites,high-risk innovative appsConsumer web applicationsApplication CharacteristicHas very spiky usageRunning application on-premises raisessecurity issuesNeeds fast access to computing resourceswith no commitmentRequires massive or global scaleStart-ups, progressive businessesD on’t want in-house ITExample: An HPC application on Microsoft AzureE N T E R P R I S EM I C R O S O F T A Z U R EDeveloper/IT AdminUsers1Create cluster1000110100110011110111110110110100011011000110100110011110111110110110100011011000110100110011110111110110110100011012Submit jobVMsVMs VMs VMsLogic Logic Logic LogicWindows Server with HPC Pack 2012MapReduce JobExample: A big data application using HDInsightE N T E R P R I S EM I C R O S O F T A Z U R EDeveloper/IT AdminHDInsight1Create Hadoop clusterVMsVMs VMs VMs1000110100110011110111110110110100011011000110100110011110111110110110100011011000110100110011110111110110110100011012Submit MapReduce jobLogicLogicLogicLogicUser3Get resultsLower costPay only for the VMs you need when you need them On-demand access to an HPC clusterWindows HPC Server provides built-in support for creating and managing a cluster on Microsoft AzureWhy do this?On-demand access to aHadoop clusterHDInsight provides built-insupport for creating andmanaging a HadoopclusterApplicationsSummarizing the scenarios▪New employee-facing applications ▪New customer-facing applications ▪New parallel applicationsConclusions▪Public cloud platforms can provide:‒Lower cost and higher reliability for infrastructure‒Better support for new applications▪At least one scenario probably has value for every enterprise right now What are you waiting for?About the SpeakerDavid Chappell is Principal of Chappell & Associates() in San Francisco, California. Through hisspeaking, writing, and consulting, he helps people around the worldunderstand, use, and make better decisions about new technology. Davidhas been the keynote speaker for more than a hundred events andconferences on five continents, and his seminars have been attended bytens of thousands of business and IT leaders, architects, and developers inforty-five countries. His books have been published in a dozen languagesand used regularly in courses at MIT, ETH Zurich, and other universities. Inhis consulting practice, he has helped clients such as Hewlett-Packard,IBM, Microsoft, Stanford University, and Target Corporation adopt newtechnologies, market new products, and educate their customers and staff.Copyright © 2015 Chappell & Associates | @DChappellAssoc。
统计学专业英语词汇完整版
统计学专业英语词汇Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚A Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Absolute deviation, 绝对离差Attributable risk, 归因危险度Absolute number, 绝对数Attribute data, 属性资料Absolute residuals, 绝对残差Attribution, 属性Acceleration array, 加速度立体阵Autocorrelation, 自相关Acceleration in an arbitrary direction, 任意方向上的加速度Autocorrelation of residuals, 残差的自相关Acceleration normal, 法向加速度Average, 平均数Acceleration space dimension, 加速度空间的维数Average confidence interval length, 平均置信区间长度Acceleration tangential, 切向加速度Average growth rate, 平均增长率Acceleration vector, 加速度向量BAcceptable hypothesis, 可接受假设Accumulation, 累积Bar chart, 条形图Accuracy, 准确度Bar graph, 条形图Actual frequency, 实际频数Base period, 基期Adaptive estimator, 自适应估计量Bayes theorem, 贝叶斯定理Addition, 相加Bell-shaped curve, 钟形曲线Addition theorem, 加法定理Bernoulli distribution, 伯努力分布Additivity, 可加性Best-trim estimator, 最好切尾估计量Adjusted rate, 调整率Bias, 偏性Adjusted value, 校正值Binary logistic regression, 二元逻辑斯蒂回归Admissible error, 容许误差Binomial distribution, 二项分布Aggregation, 聚集性Bisquare, 双平方Alternative hypothesis, 备择假设Bivariate Correlate, 二变量相关Among groups, 组间Bivariate normal distribution, 双变量正态分布Amounts, 总量Bivariate normal population, 双变量正态总体Analysis of correlation, 相关分析Biweight interval, 双权区间Analysis of covariance, 协方差分析Biweight M-estimator, 双权M 估计量Analysis of regression, 回归分析Block, 区组/配伍组Analysis of time series, 时间序列分析BMDP(Biomedical computer programs),BMDP 统计软件包Analysis of variance, 方差分析Box plots, 箱线图/箱尾图Angular transformation, 角转换Break down bound, 崩溃界/崩溃点ANOVA (analysis of variance ),方差分析CANOVA Models, 方差分析模型Arcing, 弧/弧旋Canonical correlation, 典型相关Arcsine transformation, 反正弦变换Caption, 纵标目Area under the curve, 曲线面积Case-control study, 病例对照研究AREG, 评估从一个时间点到下一个时间点回归相关时的误差Categorical variable, 分类变量ARIMA, 季节和非季节性单变量模型的极大似然估计Catenary, 悬链线Arithmetic grid paper, 算术格纸Cauchy distribution, 柯西分布Arithmetic mean, 算术平均数Cause-and-effect relationship, 因果关系Arrhenius relation, 艾恩尼斯关系Cell, 单元Assessing fit, 拟合的评估Censoring, 终检Associative laws, 结合律Center of symmetry, 对称中心Centering and scaling, 中心化和定标Comparison of bathes, 批比较Central tendency, 集中趋势Comparison value, 比较值Central value, 中心值Compartment model, 分部模型CHAID- χ2AutomaticInteractionDetector, 卡方自动交互检测Compassion, 伸缩Chance, 机遇Complement of an event, 补事件Chance error, 随机误差Complete association, 完全正相关Chance variable, 随机变量Complete dissociation, 完全不相关Characteristic equation, 特征方程Complete statistics, 完备统计量Characteristic root, 特征根Completely randomized design, 完全随机化设计Characteristic vector, 特征向量Composite event, 联合事件/复合事件Chebshev criterion of fit, 拟合的切比雪夫准则Concavity, 凹性Chernoff faces, 切尔诺夫脸谱图Conditional expectation, 条件期望Chi-square test, 卡方检验/ χ2检验Conditional likelihood, 条件似然Choleskey decomposition, 乔洛斯基分解Conditional probability, 条件概率Circle chart, 圆图Conditionally linear, 依条件线性Class interval, 组距Confidence interval, 置信区间Class mid-value, 组中值Confidence limit, 置信限Class upper limit, 组上限Confidence lower limit, 置信下限Classified variable, 分类变量Confidence upper limit, 置信上限Cluster analysis, 聚类分析Confirmatory Factor Analysis, 验证性因子分析Cluster sampling, 整群抽样Confirmatory research, 证实性实验研究Code, 代码Confounding factor, 混杂因素Coded data, 编码数据Conjoint, 联合分析Coding, 编码Consistency, 相合性Coefficient of contingency, 列联系数Consistency check, 一致性检验Coefficient of determination, 决定系数Consistent asymptotically normal estimate, 相合渐近正态估Coefficient of multiple correlation, 多重相关系数计Coefficient of partial correlation, 偏相关系数Consistent estimate, 相合估计Coefficient of production-moment correlation, 积差相关系数Constrained nonlinear regression, 受约束非线性回归Coefficient of rank correlation, 等级相关系数Constraint, 约束Coefficient of regression, 回归系数Contaminated distribution, 污染分布Coefficient of skewness, 偏度系数Contaminated Gausssian, 污染高斯分布Coefficient of variation, 变异系数Contaminated normal distribution, 污染正态分布Cohort study, 队列研究Contamination, 污染Column, 列Contamination model, 污染模型Column effect, 列效应Contingency table, 列联表Column factor, 列因素Contour, 边界线Combination pool, 合并Contribution rate, 贡献率Combinative table, 组合表Control, 对照Common factor, 共性因子Controlled experiments, 对照实验Common regression coefficient, 公共回归系数Conventional depth, 常规深度Common value, 共同值Convolution, 卷积Common variance, 公共方差Corrected factor, 校正因子Common variation, 公共变异Corrected mean, 校正均值Communality variance, 共性方差Correction coefficient, 校正系数Comparability, 可比性Correctness, 正确性统计学专业英语词汇 3 Correlation coefficient, 相关系数Dead time, 停滞期Correlation index, 相关指数Degree of freedom, 自由度Correspondence, 对应Degree of precision, 精密度Counting, 计数Degree of reliability, 可靠性程度Counts, 计数/频数Degression, 递减Covariance, 协方差Density function, 密度函数Covariant, 共变Density of datapoints, 数据点的密度Cox Regression, Cox 回归Dependent variable, 应变量/依变量/因变量Criteria for fitting, 拟合准则Depth, 深度Criteria of least squares, 最小二乘准则Derivative matrix, 导数矩阵Critical ratio, 临界比Derivative-free methods, 无导数方法Critical region, 拒绝域Design, 设计Critical value, 临界值Determinacy, 确定性Cross-over design, 交叉设计Determinant, 行列式Cross-section analysis, 横断面分析Determinant, 决定因素Cross-section survey, 横断面调查Deviation, 离差Cross tabs, 交叉表Deviation from average, 离均差Cross-tabulation table, 复合表Diagnostic plot, 诊断图Cube root, 立方根Dichotomous variable, 二分变量Cumulative distribution function, 累计分布函数Differential equation, 微分方程Cumulative probability, 累计概率Direct standardization, 直接标准化法Curvature, 曲率/弯曲Discrete variable, 离散型变量Curve fit, 曲线拟和Discriminant, 判断Curve fitting, 曲线拟合Discriminant analysis, 判别分析Curvilinear regression, 曲线回归Discriminant coefficient, 判别系数Curvilinear relation, 曲线关系Discriminant function, 判别值Cut-and-try method, 尝试法Dispersion, 散布/分散度Cycle, 周期Disproportional, 不成比例的Cyclist, 周期性Disproportionate sub-class numbers, 不成比例次级组含量D Distribution free, 分布无关性/免分布Distribution shape, 分布形状D test, D 检验Distribution-free method, 任意分布法Data acquisition, 资料收集Distributive laws, 分配律Databank, 数据库Disturbance, 随机扰动项Data capacity, 数据容量Dose response curve, 剂量反应曲线Data deficiencies, 数据缺乏Double blind method, 双盲法Data handling, 数据处理Double blind rial, 双盲试验Data manipulation, 数据处理Double exponential distribution, 双指数分布Data processing, 数据处理Double logarithmic, 双对数Data reduction, 数据缩减Downward rank, 降秩Data set, 数据集Dual-space plot, 对偶空间图Data sources, 数据来源DUD, 无导数方法Data transformation, 数据变换Duncan's new multiple range method, 新复极差法/Duncan 新Data validity, 数据有效性法Data-in, 数据输入Data-out, 数据输出统计学专业英语词汇 4 E Factorial, 阶乘Factorial design, 析因试验设计Effect, 实验效应False negative, 假阴性Eigen value, 特征值False negative error, 假阴性错误Eigen vector, 特征向量Family of distributions, 分布族Ellipse, 椭圆Family of estimators, 估计量族Empirical distribution, 经验分布Fanning, 扇面Empirical probability, 经验概率单位Fatality rate, 病死率Enumeration data, 计数资料Field investigation, 现场调查Equal sun-class number, 相等次级组含量Field survey, 现场调查Equally likely, 等可能Finite population, 有限总体Equal variance, 同变性Finite-sample, 有限样本Error, 误差/错误First derivative, 一阶导数Error of estimate, 估计误差First principal component, 第一主成分Error type I, 第一类错误First quartile, 第一四分位数Error type II, 第二类错误Fisher information, 费雪信息量Estimand, 被估量Fitted value, 拟合值Estimated error mean squares, 估计误差均方Fitting a curve, 曲线拟合Estimated error sum of squares, 估计误差平方和Fixed base, 定基Euclidean distance, 欧式距离Fluctuation, 随机起伏Event, 事件Forecast, 预测Exceptional data point, 异常数据点Four fold table, 四格表Expectation plane, 期望平面Fourth, 四分点Expectation surface, 期望曲面Fraction blow, 左侧比率Expected values, 期望值Fractional error, 相对误差Experiment, 实验Frequency, 频率Experimental sampling, 试验抽样Frequency polygon, 频数多边图Experimental unit, 试验单位Frontier point, 界限点Explanatory variable, 说明变量/解释变量Function relationship, 泛函关系Exploratory data analysis, 探索性数据分析GExplore Summarize, 探索-摘要Exponential curve, 指数曲线Gamma distribution, 伽玛分布Exponential growth, 指数式增长Gauss increment, 高斯增量Exsooth, 指数平滑方法Gaussian distribution, 高斯分布/正态分布Extended fit, 扩充拟合Gauss-Newton increment, 高斯-牛顿增量Extra parameter, 附加参数General census, 全面普查Extra polation, 外推法GENLOG(Generalized liner models), 广义线性模型Extreme observation, 末端观测值Geometric mean, 几何平均数Extremes, 极端值/极值Gini's mean difference, 基尼均差F GLM(General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度F distribution, F 分布Gradient of determinant, 行列式的梯度F test, F 检验Graeco-Latin square, 希腊拉丁方Factor, 因素/因子Grand mean, 总均值Factor analysis, 因子分析Gross errors, 重大错误Factor score, 因子得分Gross-error sensitivity, 大错敏感度统计学专业英语词汇 5 Group averages, 分组平均Initial estimate, 初始估计值Grouped data, 分组资料Initial level, 最初水平Guessed mean, 假定平均数Interaction, 交互作用H Interaction terms, 交互作用项Intercept, 截距Half-life, 半衰期Interpolation, 内插法Hampel M-estimators, 汉佩尔M 估计量Inter quartile range, 四分位距Happenstance, 偶然事件Interval estimation, 区间估计Harmonic mean, 调和均数Intervals of equal probability, 等概率区间Hazard function, 风险均数Intrinsic curvature, 固有曲率Hazard rate, 风险率Invariance, 不变性Heading, 标目Inverse matrix, 逆矩阵Heavy-tailed distribution, 重尾分布Inverse probability, 逆概率Hessian array, 海森立体阵Inverse sine transformation, 反正弦变换Heterogeneity, 不同质Iteration, 迭代Heterogeneity of variance, 方差不齐JHierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法Jacobian determinant, 雅可比行列式High-leverage point, 高杠杆率点Joint distribution function, 联合分布函数HILOGLINEAR, 多维列联表的层次对数线性模型Joint probability, 联合概率Hinge, 折叶点Joint probability distribution, 联合概率分布Histogram, 直方图KHistorical cohort study, 历史性队列研究Holes, 空洞K means method, 逐步聚类法HOMALS, 多重响应分析Kaplan-Meier, 评估事件的时间长度Homogeneity of variance, 方差齐性Kaplan-Merier chart, Kaplan-Merier 图Homogeneity test, 齐性检验Kendall' s rank correlation, Kendall 等级相关Huber M-estimators, 休伯M 估计量Kinetic, 动力学Hyperbola, 双曲线Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Hypothesis testing, 假设检验Kruskal and Wallis test, Kruskal 及Wallis 检验/多样本的秩和检验/H 检验Hypothetical universe, 假设总体Kurtosis, 峰度ILImpossible event, 不可能事件Independence, 独立性Lack of fit, 失拟Independent variable, 自变量Ladder of powers, 幂阶梯Index, 指标/指数Lag, 滞后Indirect standardization, 间接标准化法Large sample, 大样本Individual, 个体Large sample test, 大样本检验Inference band, 推断带Latin square, 拉丁方Infinite population, 无限总体Latin square design, 拉丁方设计Infinitely great, 无穷大Leakage, 泄漏Infinitely small, 无穷小Least favorable configuration, 最不利构形Influence curve, 影响曲线Least favorable distribution, 最不利分布Information capacity, 信息容量Least significant difference, 最小显著差法Initial condition, 初始条件Least square method, 最小二乘法统计学专业英语词汇 6 Least-absolute-residuals estimates, 最小绝对残差估计Marginal probability, 边缘概率Least-absolute-residuals fit, 最小绝对残差拟合Marginal probability distribution, 边缘概率分布Least-absolute-residuals line, 最小绝对残差线Matched data, 配对资料Legend, 图例Matched distribution, 匹配过分布L-estimator,L 估计量Matching of distribution, 分布的匹配L-estimator of location, 位置L 估计量Matching of transformation, 变换的匹配L-estimator of scale, 尺度L 估计量Mathematical expectation, 数学期望Level, 水平Mathematical model, 数学模型Life expectance, 预期期望寿命Maximum L-estimator, 极大L 估计量Life table, 寿命表Maximum likelihood method, 最大似然法Life table method, 生命表法Mean, 均数Light-taile distribution, 轻尾分布Mean squares between groups, 组间均方Likelihood function, 似然函数Mean squares within group, 组内均方Likelihood ratio, 似然比Means (Compare means), 均值-均值比较Line graph, 线图Median, 中位数Linear correlation, 直线相关Median effective dose, 半数效量Linear equation, 线性方程Median lethal dose, 半数致死量Linear programming, 线性规划Median polish, 中位数平滑Linear regression, 直线回归/线性回归Median test, 中位数检验Linear trend, 线性趋势Minimal sufficient statistic, 最小充分统计量Loading, 载荷Minimum distance estimation, 最小距离估计Location and scale equi variance, 位置尺度同变性Minimum effective dose, 最小有效量Location equi variance, 位置同变性Minimum lethal dose, 最小致死量Location invariance, 位置不变性Minimum variance estimator, 最小方差估计量Location scale family, 位置尺度族MINITAB, 统计软件包Log rank test, 时序检验Minor heading, 宾词标目Logarithmic curve, 对数曲线Missing data, 缺失值Logarithmic normal distribution, 对数正态分布Model specification, 模型的确定Logarithmic scale, 对数尺度Modeling Statistics , 模型统计Logarithmic transformation, 对数变换Models for outliers, 离群值模型Logic check, 逻辑检查Modifying the model, 模型的修正Logistic distribution, 逻辑斯蒂分布Modulus of continuity, 连续性模Logit transformation, Logit 转换Morbidity, 发病率LOGLINEAR, 多维列联表通用模型Most favorable configuration, 最有利构形Lognormal distribution, 对数正态分布Multidimensional Scaling (ASCAL), 多维尺度/多维标度Lost function, 损失函数Multinomial Logistic Regression , 多项逻辑斯蒂回归Low correlation, 低度相关Multiple comparison, 多重比较Lower limit, 下限Multiple correlation , 复相关Lowest-attained variance, 最小可达方差Multiple covariance, 多元协方差LSD, 最小显著差法的简称Multiple linear regression, 多元线性回归Lurking variable, 潜在变量Multiple response , 多重选项M Multiple solutions, 多解Multiplication theorem, 乘法定理Main effect, 主效应Multiresponse, 多元响应Major heading, 主辞标目Multi-stage sampling, 多阶段抽样Marginal density function, 边缘密度函数Multivariate T distribution, 多元T 分布统计学专业英语词汇7 Mutual exclusive, 互不相容Outliers, 极端值Mutual independence, 互相独立OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度NPNatural boundary, 自然边界Natural dead, 自然死亡Paired design, 配对设计Natural zero, 自然零Paired sample, 配对样本Negative correlation, 负相关Pairwise slopes, 成对斜率Negative linear correlation, 负线性相关Parabola, 抛物线Negatively skewed, 负偏Parallel tests, 平行试验Newman-Keuls method, q 检验Parameter, 参数NK method, q 检验Parametric statistics, 参数统计No statistical significance, 无统计意义Parametric test, 参数检验Nominal variable, 名义变量Partial correlation, 偏相关Nonconstancy of variability, 变异的非定常性Partial regression, 偏回归Nonlinear regression, 非线性相关Partial sorting, 偏排序Nonparametric statistics, 非参数统计Partials residuals, 偏残差Nonparametric test, 非参数检验Pattern, 模式Normal deviate, 正态离差Pearson curves, 皮尔逊曲线Normal distribution, 正态分布Peeling, 退层Normal equation, 正规方程组Percent bar graph, 百分条形图Normal ranges, 正常范围Percentage, 百分比Normal value, 正常值Percentile, 百分位数Nuisance parameter, 多余参数/讨厌参数Percentile curves, 百分位曲线Null hypothesis, 无效假设Periodicity, 周期性Numerical variable, 数值变量Permutation, 排列O P-estimator,P 估计量Pie graph, 饼图Objective function, 目标函数Pitman estimator, 皮特曼估计量Observation unit, 观察单位Pivot, 枢轴量Observed value, 观察值Planar, 平坦One sided test, 单侧检验Planar assumption, 平面的假设One-way analysis of variance, 单因素方差分析PLANCARDS, 生成试验的计划卡One way ANOVA , 单因素方差分析Point estimation, 点估计Open sequential trial, 开放型序贯设计Poisson distribution, 泊松分布Optrim, 优切尾Polishing, 平滑Optrim efficiency, 优切尾效率Polled standard deviation, 合并标准差Order statistics, 顺序统计量Polled variance, 合并方差Ordered categories, 有序分类Polygon, 多边图Ordinal logistic regression , 序数逻辑斯蒂回归Polynomial, 多项式Ordinal variable, 有序变量Polynomial curve, 多项式曲线Orthogonal basis, 正交基Population, 总体Orthogonal design, 正交试验设计Population attributable risk, 人群归因危险度Orthogonality conditions, 正交条件Positive correlation, 正相关ORTHOPLAN, 正交设计Positively skewed, 正偏Outlier cutoffs, 离群值截断点Posterior distribution, 后验分布Power of a test, 检验效能Ratio, 比例Precision, 精密度Raw data, 原始资料Predicted value, 预测值Raw residual, 原始残差Preliminary analysis, 预备性分析Rayleigh' s test, 雷氏检验Principal component analysis, 主成分分析Rayleigh' s Z,雷氏Z 值Prior distribution, 先验分布Reciprocal, 倒数Prior probability, 先验概率Reciprocal transformation, 倒数变换Probabilistic model, 概率模型Recording, 记录probability, 概率Redescending estimators, 回降估计量Probability density, 概率密度Reducing dimensions, 降维Product moment, 乘积矩/协方差Re-expression, 重新表达Profile trace, 截面迹图Reference set, 标准组Proportion, 比/构成比Region of acceptance, 接受域Proportion allocation in stratified random sampling, 按比例分Regression coefficient, 回归系数层随机抽样Regression sum of square, 回归平方和Proportionate, 成比例Rejection point, 拒绝点Proportionate sub-class numbers, 成比例次级组含量Relative dispersion, 相对离散度Prospective study, 前瞻性调查Relative number, 相对数Proximities, 亲近性Reliability, 可靠性Pseudo F test, 近似F 检验Reparametrization, 重新设置参数Pseudo model, 近似模型Replication, 重复Pseudo sigma, 伪标准差Report Summaries, 报告摘要Purposive sampling, 有目的抽样Residual sum of square, 剩余平方和Q Resistance, 耐抗性Resistant line, 耐抗线QR decomposition, QR 分解Resistant technique, 耐抗技术Quadratic approximation, 二次近似R-estimator of location, 位置R 估计量Qualitative classification, 属性分类R-estimator of scale, 尺度R 估计量Qualitative method, 定性方法Retrospective study, 回顾性调查Quantile-quantile plot, 分位数-分位数图/Q-Q 图Ridge trace, 岭迹Quantitative analysis, 定量分析Ridit analysis , Ridit 分析Quartile, 四分位数Rotation, 旋转Quick Cluster, 快速聚类Rounding, 舍入R Row, 行Row effects, 行效应Radix sort, 基数排序Row factor, 行因素Random allocation, 随机化分组RXC table, RXC 表Random blocks design, 随机区组设计SRandom event, 随机事件Randomization, 随机化Sample, 样本Range, 极差/全距Sample regression coefficient, 样本回归系数Rank correlation, 等级相关Sample size, 样本量Rank sum test, 秩和检验Sample standard deviation, 样本标准差Rank test, 秩检验Sampling error, 抽样误差Ranked data, 等级资料SAS(Statistical analysis system ),SAS 统计软件包Rate, 比率Scale, 尺度/量表Scatter diagram, 散点图Spurious correlation, 假性相关Schematic plot, 示意图/简图Square root transformation, 平方根变换Score test, 计分检验Stabilizing variance, 稳定方差Screening, 筛检Standard deviation, 标准差SEASON, 季节分析Standard error, 标准误Second derivative, 二阶导数Standard error of difference, 差别的标准误Second principal component, 第二主成分Standard error of estimate, 标准估计误差SEM (Structural equation modeling), 结构化方程模型Standard error of rate, 率的标准误Semi-logarithmic graph, 半对数图Standard normal distribution, 标准正态分布Semi-logarithmic paper, 半对数格纸Standardization, 标准化Sensitivity curve, 敏感度曲线Starting value, 起始值Sequential analysis, 贯序分析Statistic, 统计量Sequential data set, 顺序数据集Statistical control, 统计控制Sequential design, 贯序设计Statistical graph, 统计图Sequential method, 贯序法Statistical inference, 统计推断Sequential test, 贯序检验法Statistical table, 统计表Serial tests, 系列试验Steepest descent, 最速下降法Short-cut method, 简捷法Stem and leaf display, 茎叶图Sigmoid curve, S 形曲线Step factor, 步长因子Sign function, 正负号函数Stepwise regression, 逐步回归Sign test, 符号检验Storage, 存Signed rank, 符号秩Strata, 层(复数)Significance test, 显著性检验Stratified sampling, 分层抽样Significant figure, 有效数字Stratified sampling, 分层抽样Simple cluster sampling, 简单整群抽样Strength, 强度Simple correlation, 简单相关Stringency, 严密性Simple random sampling, 简单随机抽样Structural relationship, 结构关系Simple regression, 简单回归Studentized residual, 学生化残差/t 化残差simple table, 简单表Sub-class numbers, 次级组含量Sine estimator, 正弦Subdividing, 分割Single-valued estimate, 单值估计Sufficient statistic, 充分统计量Singular matrix, 奇异矩阵Sum of products, 积和Skewed distribution, 偏斜分布Sum of squares, 离差平方和Skewness, 偏度Sum of squares about regression, 回归平方和Slash distribution, 斜线分布Sum of squares between groups, 组间平方和Slope, 斜率Sum of squares of partial regression, 偏回归平方和Smirnov test, 斯米尔诺夫检验Sure event, 必然事件Source of variation, 变异来源Survey, 调查Spearman rank correlation, 斯皮尔曼等级相关Survival, 生存分析Specific factor, 特殊因子Survival rate, 生存率Specific factor variance, 特殊因子方差Suspended root gram, 悬吊根图Spectra , 频谱Symmetry, 对称Spherical distribution, 球型正态分布Systematic error, 系统误差Spread, 展布Systematic sampling, 系统抽样SPSS(Statistical package for the social science), SPSS Tags, 标签统计软件包Tail area, 尾部面积Tail length, 尾长Vague concept, 模糊概念Tail weight, 尾重Validity, 有效性Tangent line, 切线VARCOMP (Variance component estimation), 方差元素估计Target distribution, 目标分布Variability, 变异性Taylor series, 泰勒级数Variable, 变量Test( 检验) Variance, 方差Test of linearity, 线性检验Variation, 变异Tendency of dispersion, 离散趋势Varimax orthogonal rotation, 方差最大正交旋转Testing of hypotheses, 假设检验Volume of distribution, 容积Theoretical frequency, 理论频数W test, W 检验Time series, 时间序列Weibull distribution, 威布尔分布Tolerance interval, 容忍区间Weight, 权数Tolerance lower limit, 容忍下限Weighted Chi-square test, 加权卡方检验/Cochran 检验Tolerance upper limit, 容忍上限Weighted linear regression method, 加权直线回归Torsion, 扰率Weighted mean, 加权平均数Total sum of square, 总平方和Weighted mean square, 加权平均方差Total variation, 总变异Weighted sum of square, 加权平方和Transformation, 转换Weighting coefficient, 权重系数Treatment, 处理Weighting method, 加权法Trend, 趋势W-estimation, W 估计量Trend of percentage, 百分比趋势W-estimation of location, 位置W 估计量Trial, 试验Width, 宽度Trial and error method, 试错法Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Tuning constant, 细调常数Wild point, 野点/狂点Two sided test, 双向检验Wild value, 野值/狂值Two-stage least squares, 二阶最小平方Winsorized mean, 缩尾均值Two-stage sampling, 二阶段抽样Withdraw, 失访Two-tailed test, 双侧检验Youden's index, 尤登指数Two-way analysis of variance, 双因素方差分析Z test, Z 检验Two-way table, 双向表Zero correlation, 零相关Type I error, 一类错误/ α错误Z-transformation, Z 变换Type II error, 二类错误/ β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Unweighted least squares, 未加权最小平方法Upper limit, 上限Upward rank, 升秩。
六西格玛术语缩写中英对照
完全随机
联合事
复合事
凹性 条件期
条件似
条件概率 依条件线
置信区间 置信限 置信下限 置信上限
14
短语和缩写
• Confirmatory Factor Analysis 子分析
验证性因
• Confirmatory research 验研究
证实性实
• Confounding factor
混杂因素
• Conjoint 析
边界线 贡献率
对照 对照实验 常规深度
卷积 校正因
校正均值
16
短语和缩写
• Correction coefficient • Correctness • Correlation • Correlation coefficient
数 • Correlation index
数 • Correspondence • COPQ (cost of poor quality) • Counting • Counts • Covariance • Covariant • Cox Regression
计量 • Between-group variation • Bias • Binary logistic regression
蒂回归 • Binomial distribution • Binomial tests
验 • Bisquare • Bivariate Correlate
关 • Bivariate normal distribution
归因危险
• Attribute data 离散数据
属性资料/
5
短语和缩写
• ARIMA 似然估计
季节和非季节性单变量模型的极大
Cogent
Data Sheet Cogent® μScale TFF SystemEasy-to-use, semi-automated benchtopTFF system for both micro-scale processdevelopment and everyday low-volumeultrafiltration/diafiltration workThe Cogent® µScale tangential flow filtration (TFF)system is an easy-to-use, semi-automated benchtopsolution that has been designed to fully support TFFprocess development at the “micro-scale” using up tothree Pellicon® 3 88 cm2 cassettes (264 cm2). It is alsoan excellent tool for streamlining everyday low volumeultrafiltration/concentration and diafiltration (UF/DF)work in the biopharmaceutical research environment.With a low minimum working volume (16 mL*),the ability to operate at feed pressures up to 80 psi(5.5 bar) and low pulsation (≤ 3 psi), the Cogent®µScale TFF system enables both scaling studies andlow volume UF/DF work using Pellicon® 3 88 cm2TFF cassettes. It is ideally suited for purifying andconcentrating your monoclonal antibodies, recombinantproteins, vaccines, gene therapy constructs, bloodserum products, and other cell-derived components.• Versatile system — Ideally suited for bothscaling studies and low volume UF/DF work• Enhanced productivity — User-configurablealarm set points and automateddata acquisition• Easy-to-use — Intuitive, multilingualdisplay and touchscreen interface*Reference minimum working volume specification.The life science business of Merckoperates as MilliporeSigma in theU.S. and Canada.Designed for Everyday UseAn intuitive, multilingual display and touchscreen interface makes the Cogent® µScale TFF system easy to operate, and the user-configurable alarm set-points and automated data acquisition enable you to be more productive. Time-stamped data for all operational parameters, including alarm and event history are automatically captured by the system and can be easily uploaded in a tab delimited/CSV file format directly to your PC, and imported into standard spreadsheet programs such as Microsoft®Excel® software.The semi-automated system can be run at either a fixed pump speed or at a set DeltaP via an automated control loop. Alarm set-points for feed and retentate pressure, DeltaP, TMP, and filtrate flow/weight (present only with filtrate weight scale option) include four settings that either “alert” you to changing conditions (Hi/Lo settings), or “shut down” the process (HiHi/ LoLo settings) if desired. When an alarm conditionis triggered, a message appears on the touchscreen display. An audible alarm can also be activated. The system also includes an E-stop that will immediately shut down the process if needed.With the optional filtrate weight scale, you can measure filtrate flow and weight. You also have the ability to automatically shut down your process when a target weight/product concentration has been achieved. The P&ID screen monitors all active parameters, including: pump speed, mixer speed, feed and retentate pressures, temperature, calculated feed flow rate, DeltaP, TMP, and calculated filtrate flow and weight (only with weight scale option), providing an easy way to monitor your process in real time. And through a separate trend screen you can quickly see how key process parameters have changed over the courseof a run, facilitating process development.The Cogent® µScale TFF system includes a 1L polypropylene tank with a removable vacuum seallid that enables vacuum diafiltration/buffer exchange and/or fed batch processing of samples up to 5L or more. Also included with the system is a filter holder for the Pellicon® 3 88 cm2 cassettes, and a complete high-pressure tubing assembly capable of running up to 80 psi (5.5 bar), enabling you to run at higher DeltaP and TMP settings.Real-World TFF Operations1. Easily create accurate scale-down models forprocess development, membrane screeningand process characterization at the micro-scale– Enables you to do more with less product2. Feed pressures up to 80 psi– Allows higher DeltaP and TMP processing3. Robust design with minimalmaintenance requirement1L polypropylene tankDual-channel pump head for low pulsation® holder for Pellicon® 3 88 cm2 cassettes2Optimized for Process Developmentand Research ApplicationsThe Cogent® µScale TFF system is designed to meet your real-world process development and low volume sample preparation requirements. This system will support all your TFF operations including fed batch, diafiltration and concentration. System set-up is quick and easy, and the user interface and user-defined control parameters enable you to execute development work quickly, safely, reliably and reproducibly. The robust flow path supports flow rates from 17 mL/min to 330 mL/min at operating pressures up to 80 psig (5.5 bar) with very low pulsation. The system also has an extremely low minimum recirculation andhold-up volume for low volume processing and maximum product recovery, and with a compact footprint, it is easy to use in any research orlab environment.CleanabilityThe fluid path is drainable and designed forClean-in-Place (CIP) using industry standard cleaning agents. If cross-contamination is a concern, the tubing assembly and one-liter polypropylene tank assembly can be easily replaced in just a few minutes.Automated Data Capture and ExportThe Cogent® µScale TFF system automatically captures time-stamped data for the following operational paramaters: Feed Pressure, Retentate Pressure, DeltaP, TMP, Feed Flow, Pump Speed, and Temperature. With the Weight Scale option, Filtrate Flow Rate and Filtrate Weight are captured as well.In addition, alarm history and event history (e.g., user login, calibration changes) are captured in separate time-stamped files. These tab delimited/CSV files can then be manually or automatically uploaded directly to a PC for import into standard spreadsheet programs.Figure 1. Cogent® µScale system P&ID screen provides real-time monitoring of your TFF process.SpecificationsSupported TFFDevicesPellicon® 388 cm2 cassettes(88 cm2 to 264 cm2)Pellicon® XL 50 device(50 cm2 to 150 cm2) Pellicon® CassetteHolder includedwith systemHolds Pellicon® 388 cm2 cassettesFiltration Area0.005 – 0.0264 m2Tank Volume1LMinimumWorking Volume16 mLHold-up Volume< 3 mL (excluding cassette)Process Temperature 4 to 50°CPump Flow Rate17 – 330 mL/minSensors Feed andretentate pressure0 – 5.5 bar(0 – 80 psig)Temperature0 – 50°C (32 – 122°F) Retentate Controland Isolation ValvesManualWeight Scale 6 kg maximum capacityLanguagesSupportedEnglish, French, German, Spanish, Italian,Chinese, and JapaneseDimensions Width41 cm (16.14 in.)Depth48 cm (18.89 in.)Height62 cm (24.40 in.)Weight with holder30 kg (66 lbs.) Power Supply100-240 VAC, 50-60Hz, 4 / 2AWetted Materialsof ConstructionFilter holder316L stainless steelTubing Silicone (platinum-cured) and GORESTA-PURE®(platinum-curedsilicone expandedPTFE) plasticLuer fittings PolypropylenePressure sensors TitaniumFlow cells PolysulfoneStir bar PTFEO-rings SiliconeVent valve Polycarbonate Wetted Materials All wetted polymers and gaskets areproduced with FDA Title 21 CFR, paragraph177RegulatoryInformationThe Cogent® µScale system meets therequirements of the Low Voltage Directive(LVD) (2014/35/EU) and Directive 2014/30/EU for Electromagnetic compatibility (EMC)along with the RoHS 2, Directive 2011/65/EU. Please review the product's Declaration ofConformity for the most up to date list of alllaws, regulations and directives in which theproduct meets and/or is evaluated too.3Lit. No. DS0003EN00 Ver. 4.02018-0419310/2018Ordering InformationTubing subassembly(includes all fittings and pinch valves)CUP0302Pump tubing kitCUP0311Dual-channel peristaltic pump head CUP0303Temperature/pressure sensor subassembly CUP0304Pressure sensor subassemblyCUP0305Retentate valve/manifold subassembly CUP0306Set of fuses for main switchCUP0307Annual maintenance spare parts kit (PLC battery & O-rings)CUP0310PLC battery & media memory card CMP1415Vent filtersSLFG025LS Pellicon ® 3 88 cm 2 cassette filter holder XX42PMICRO Pellicon ® XL 50 standXXPXLSTND Zone 1 (Travel of less than 50 miles from a service office)SVCTB783K7Z1Zone 2 (Travel of more than 50 milesbut less than 200 miles from a service office)SVCTB783K7Z2Zone 3 (Travel of more than 200 miles from a service office)SVCTB783K7Z3*Contact your sales representative for specific zone information.© 2018 Merck KGaA, Darmstadt, Germany and/or its affiliates. All Rights Reserved. Merck, the vibrant M, Millipore, Cogent and Pellicon are trademarks of Merck KGaA, Darmstadt, Germany or its affiliates. All other trademarks are the property of their respective owners. Detailed information on trademarks is available via publicly accessible resources.Merck KGaAFrankfurter Strasse 250 64293 Darmstadt, GermanyTo Place an Order or Receive Technical AssistancePlease visit/contactPS For additional information, please visit 。
统计术语中英文对照
统计术语中英文对照[全]Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差 ARIMA, 季节和非季节性单变量模型的极大似然估计 Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量 Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型 Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型 Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐 Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究 Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关 Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型 Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
NONLINEARTIMESERIESANALYSIS
NONLINEARTIMESERIESANALYSIS More informationNONLINEAR TIME SERIES ANALYSISThis book represents a modern approach to time series analysis which is based on the theory of dynamical systems.It starts from a sound outline of the underlying theory to arrive at very practical issues,which are illustrated using a large number of empirical data sets taken from various?elds.This book will hence be highly useful for scientists and engineers from all disciplines who study time variable signals, including the earth,life and social sciences.The paradigm of deterministic chaos has in?uenced thinking in many?elds of science.Chaotic systems show rich and surprising mathematical structures.In the applied sciences,deterministic chaos provides a striking explanation for irregular temporal behaviour and anomalies in systems which do not seem to be inherently stochastic.The most direct link between chaos theory and the real world is the anal-ysis of time series from real systems in terms of nonlinear dynamics.Experimental technique and data analysis have seen such dramatic progress that,by now,most fundamental properties of nonlinear dynamical systems have been observed in the laboratory.Great efforts are being made to exploit ideas from chaos theory where-ver the data display more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics and many other sciences.This revised edition has been signi?cantly rewritten an expanded,including several new chapters.In view of applications,the most relevant novelties will be the treatment of non-stationary data sets and of nonlinear stochastic processes inside the framework of a state space reconstruction by the method of delays.Hence,non-linear time series analysis has left the rather narrow niche of strictly deterministic systems.Moreover,the analysis of multivariate data sets has gained more atten-tion.For a direct application of the methods of this book to the reader’s own data sets,this book closely refers to the publicly available software package TISEAN. The availability of this software will facilitate the solution of the exercises,so that readers now can easily gain their own experience with the analysis of data sets. Holger Kantz,born in November1960,received his diploma in physics fromthe University of Wuppertal in January1986with a thesis on transient chaos.In January1989he obtained his Ph.D.in theoretical physics from the same place, having worked under the supervision of Peter Grassberger on Hamiltonian many-particle dynamics.During his postdoctoral time,he spent one year on a Marie Curie fellowship of the European Union at the physics department of the University ofMore informationFlorence in Italy.In January1995he took up an appointment at the newly foundedMax Planck Institute for the Physics of Complex Systems in Dresden,where he established the research group‘Nonlinear Dynamics and Time Series Analysis’.In1996he received his venia legendi and in2002he became adjunct professorin theoretical physics at Wuppertal University.In addition to time series analysis,he works on low-and high-dimensional nonlinear dynamics and its applications.More recently,he has been trying to bridge the gap between dynamics and statis-tical physics.He has(co-)authored more than75peer-reviewed articles in scien-ti?c journals and holds two international patents.For up-to-date information seehttp://www.mpipks-dresden.mpg.de/mpi-doc/kantzgruppe.html.Thomas Schreiber,born1963,did his diploma work with Peter Grassberger at Wuppertal University on phase transitions and information transport in spatio-temporal chaos.He joined the chaos group of Predrag Cvitanovi′c at the Niels Bohr Institute in Copenhagen to study periodic orbit theory of diffusion and anomalous transport.There he also developed a strong interest in real-world applications ofchaos theory,leading to his Ph.D.thesis on nonlinear time series analysis(Univer-sity of Wuppertal,1994).As a research assistant at Wuppertal University and during several extended appointments at the Max Planck Institute for the Physics of Com-plex Systems in Dresden he published numerous research articles on time series methods and applications ranging from physiology to the stock market.His habil-itation thesis(University of Wuppertal)appeared as a review in Physics Reportsin1999.Thomas Schreiber has extensive experience teaching nonlinear dynamicsto students and experts from various?elds and at all levels.Recently,he has left academia to undertake industrial research.NONLINEAR TIME SERIES ANALYSIS HOLGER KANTZ AND THOMAS SCHREIBER Max Planck Institute for the Physics of Complex Systems,DresdenMore informationMore informationpublished by the press syndicate of the university of cambridgeThe Pitt Building,Trumpington Street,Cambridge,United Kingdomcambridge university pressThe Edinburgh Building,Cambridge CB22RU,UK40West20th Street,New York,NY10011–4211,USA477Williamstown Road,Port Melbourne,VIC3207,AustraliaRuiz de Alarc′o n13,28014Madrid,SpainDock House,The Waterfront,Cape Town8001,South Africa/doc/bedb5fe3524de518964b7d07.htmlC Holger Kantz and Thomas Schreiber,2000,2003This book is in copyright.Subject to statutory exceptionand to the provisions of relevant collective licensing agreements,no reproduction of any part may take place withoutthe written permission of Cambridge University Press.First published2000Second edition published2003Printed in the United Kingdom at the University Press,CambridgeTypeface Times11/14pt.System L A T E X2ε[tb]A catalogue record for this book is available from the British LibraryLibrary of Congress Cataloguing in Publication dataKantz,Holger,1960–Nonlinear time series analysis/Holger Kantz and Thomas Schreiber.–[2nd ed.].p.cm.Includes bibliographical references and index.ISBN0521821509–ISBN0521529026(paperback)1.Time-series analysis.2.Nonlinear theories.I.Schreiber,Thomas,1963–II.TitleQA280.K3552003519.5 5–dc212003044031ISBN0521821509hardbackISBN0521529026paperbackThe publisher has used its best endeavours to ensure that the URLs for external websites referred to in this bookare correct and active at the time of going to press.However,the publisher has no responsibility for the websites and can make no guarantee that a site will remain live or that the content is or will remain appropriate.More informationContentsPreface to the?rst edition page xiPreface to the second edition xiii Acknowledgements xvI Basic topics11Introduction:why nonlinear methods?32Linear tools and general considerations132.1Stationarity and sampling132.2Testing for stationarity152.3Linear correlations and the power spectrum182.3.1Stationarity and the low-frequency component in the power spectrum232.4Linear?lters242.5Linear predictions273Phase space methods303.1Determinism:uniqueness in phase space303.2Delay reconstruction353.3Finding a good embedding363.3.1False neighbours373.3.2The time lag393.4Visual inspection of data393.5Poincar′e surface of section413.6Recurrence plots434Determinism and predictability484.1Sources of predictability484.2Simple nonlinear prediction algorithm504.3Veri?cation of successful prediction534.4Cross-prediction errors:probing stationarity564.5Simple nonlinear noise reduction58vMore informationvi Contents5Instability:Lyapunov exponents655.1Sensitive dependence on initial conditions655.2Exponential divergence665.3Measuring the maximal exponent from data696Self-similarity:dimensions756.1Attractor geometry and fractals756.2Correlation dimension776.3Correlation sum from a time series786.4Interpretation and pitfalls826.5Temporal correlations,non-stationarity,and space time separation plots876.6Practical considerations916.7A useful application:determination of the noise level using the correlation integral926.8Multi-scale or self-similar signals956.8.1Scaling laws966.8.2Detrended?uctuation analysis1007Using nonlinear methods when determinism is weak1057.1Testing for nonlinearity with surrogate data1077.1.1The null hypothesis1097.1.2How to make surrogate data sets1107.1.3Which statistics to use1137.1.4What can go wrong1157.1.5What we have learned1177.2Nonlinear statistics for system discrimination1187.3Extracting qualitative information from a time series1218Selected nonlinear phenomena1268.1Robustness and limit cycles1268.2Coexistence of attractors1288.3Transients1288.4Intermittency1298.5Structural stability1338.6Bifurcations1358.7Quasi-periodicity139II Advanced topics1419Advanced embedding methods1439.1Embedding theorems1439.1.1Whitney’s embedding theorem1449.1.2Takens’s delay embedding theorem1469.2The time lag148More informationContents vii9.3Filtered delay embeddings1529.3.1Derivative coordinates1529.3.2Principal component analysis1549.4Fluctuating time intervals1589.5Multichannel measurements1599.5.1Equivalent variables at different positions1609.5.2Variables with different physical meanings1619.5.3Distributed systems1619.6Embedding of interspike intervals1629.7High dimensional chaos and the limitations of the time delay embedding1659.8Embedding for systems with time delayed feedback17110Chaotic data and noise17410.1Measurement noise and dynamical noise17410.2Effects of noise17510.3Nonlinear noise reduction17810.3.1Noise reduction by gradient descent17910.3.2Local projective noise reduction18010.3.3Implementation of locally projective noise reduction183 10.3.4How much noise is taken out?18610.3.5Consistency tests19110.4An application:foetal ECG extraction19311More about invariant quantities19711.1Ergodicity and strange attractors19711.2Lyapunov exponents II19911.2.1The spectrum of Lyapunov exponents and invariant manifolds20011.2.2Flows versus maps20211.2.3Tangent space method20311.2.4Spurious exponents20511.2.5Almost two dimensional?ows21111.3Dimensions II21211.3.1Generalised dimensions,multi-fractals213 11.3.2Information dimension from a time series215 11.4Entropies21711.4.1Chaos and the?ow of information21711.4.2Entropies of a static distribution21811.4.3The Kolmogorov–Sinai entropy22011.4.4The -entropy per unit time22211.4.5Entropies from time series data226More informationviii Contents11.5How things are related22911.5.1Pesin’s identity22911.5.2Kaplan–Yorke conjecture23112Modelling and forecasting23412.1Linear stochastic models and?lters23612.1.1Linear?lters23712.1.2Nonlinear?lters23912.2Deterministic dynamics24012.3Local methods in phase space24112.3.1Almost model free methods24112.3.2Local linear?ts24212.4Global nonlinear models24412.4.1Polynomials24412.4.2Radial basis functions24512.4.3Neural networks24612.4.4What to do in practice24812.5Improved cost functions24912.5.1Over?tting and model costs24912.5.2The errors-in-variables problem25112.5.3Modelling versus prediction25312.6Model veri?cation25312.7Nonlinear stochastic processes from data256 12.7.1Fokker–Planck equations from data257 12.7.2Markov chains in embedding space259 12.7.3No embedding theorem for Markov chains26012.7.4Predictions for Markov chain data26112.7.5Modelling Markov chain data26212.7.6Choosing embedding parameters for Markov chains263 12.7.7Application:prediction of surface wind velocities26412.8Predicting prediction errors26712.8.1Predictability map26712.8.2Individual error prediction26812.9Multi-step predictions versus iterated one-step predictions271 13Non-stationary signals27513.1Detecting non-stationarity27613.1.1Making non-stationary data stationary27913.2Over-embedding28013.2.1Deterministic systems with parameter drift28013.2.2Markov chain with parameter drift28113.2.3Data analysis in over-embedding spaces283More informationContents ix13.2.4Application:noise reduction for human voice28613.3Parameter spaces from data28814Coupling and synchronisation of nonlinear systems29214.1Measures for interdependence29214.2Transfer entropy29714.3Synchronisation29915Chaos control30415.1Unstable periodic orbits and their invariant manifolds306 15.1.1Locating periodic orbits30615.1.2Stable/unstable manifolds from data31215.2OGY-control and derivates31315.3Variants of OGY-control31615.4Delayed feedback31715.5Tracking31815.6Related aspects319A Using the TISEAN programs321A.1Information relevant to most of the routines322A.1.1Ef?cient neighbour searching322A.1.2Re-occurring command options325A.2Second-order statistics and linear models326 A.3Phase space tools327A.4Prediction and modelling329A.4.1Locally constant predictor329A.4.2Locally linear prediction329A.4.3Global nonlinear models330A.5Lyapunov exponents331A.6Dimensions and entropies331A.6.1The correlation sum331A.6.2Information dimension,?xed mass algorithm332 A.6.3Entropies333A.7Surrogate data and test statistics334A.8Noise reduction335A.9Finding unstable periodic orbits336A.10Multivariate data336B Description of the experimental data sets338B.1Lorenz-like chaos in an NH3laser338B.2Chaos in a periodically modulated NMR laser340 B.3Vibrating string342B.4Taylor–Couette?ow342B.5Multichannel physiological data343More informationx ContentsB.6Heart rate during atrial?brillation343B.7Human electrocardiogram(ECG)344B.8Phonation data345B.9Postural control data345B.10Autonomous CO2laser with feedback345B.11Nonlinear electric resonance circuit346B.12Frequency doubling solid state laser348B.13Surface wind velocities349References350Index365More informationPreface to the?rst editionThe paradigm of deterministic chaos has in?uenced thinking in many?elds of sci-ence.As mathematical objects,chaotic systems show rich and surprising structures. Most appealing for researchers in the applied sciences is the fact that determinis-tic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic.The most direct link between chaos theory and the real world is the analysis oftime series from real systems in terms of nonlinear dynamics.On the one hand, experimental technique and data analysis have seen such dramatic progress that, by now,most fundamental properties of nonlinear dynamical systems have been observed in the laboratory.On the other hand,great efforts are being made to exploit ideas from chaos theory in cases where the system is not necessarily deterministic but the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics,and many other sciences.In all these?elds,even simple models,be they microscopic or phenomenological, can create extremely complicated dynamics.How can one verify that one’s model is a good counterpart to the equally complicated signal that one receives from nature? Very often,good models are lacking and one has to study the system just from the observations made in a single time series,which is the case for most non-laboratory systems in particular.The theory of nonlinear dynamical systems provides new tools and quantities for the characterisation of irregular time series data.The scope of these methods ranges from invariants such as Lyapunov exponents and dimensions which yield an accurate description of the structure of a system(provided thedata are of high quality)to statistical techniques which allow for classi?cation and diagnosis even in situations where determinism is almost lacking.This book provides the experimental researcher in nonlinear dynamics with meth-ods for processing,enhancing,and analysing the measured signals.The theorist will be offered discussions about the practical applicability of mathematical results.The xiMore informationxii Preface to the?rst editiontime series analyst in economics,meteorology,and other?elds will?nd inspira-tion for the development of new prediction algorithms.Some of the techniques presented here have also been considered as possible diagnostic tools in clinical re-search.We will adopt a critical but constructive point of view,pointing out ways of obtaining more meaningful results with limited data.We hope that everybody who has a time series problem which cannot be solved by traditional,linear methods will?nd inspiring material in this book.Dresden and WuppertalNovember1996More informationPreface to the second editionIn a?eld as dynamic as nonlinear science,new ideas,methods and experiments emerge constantly and the focus of interest shifts accordingly.There is a continuous stream of new results,and existing knowledge is seen from a different angle after very few years.Five years after the?rst edition of“Nonlinear Time Series Analysis”we feel that the?eld has matured in a way that deserves being re?ected in a second edition.The modi?cation that is most immediately visible is that the program listingshave been be replaced by a thorough discussion of the publicly available software TISEAN.Already a few months after the?rst edition appeared,it became clearthat most users would need something more convenient to use than the bare library routines printed in the book.Thus,together with Rainer Hegger we prepared stand-alone routines based on the book but with input/output functionality and advanced features.The?rst public release was made available in1998and subsequent releases are in widespread use now.Today,TISEAN is a mature piece of software that covers much more than the programs we gave in the?rst edition.Now,readerscan immediately apply most methods studied in the book on their own data using TISEAN programs.By replacing the somewhat terse program listings by minute instructions of the proper use of the TISEAN routines,the link between book and software is strengthened,supposedly to the bene?t of the readers and users.Hence we recommend a download and installation of the package,such that the exercises can be readily done by help of these ready-to-use routines.The current edition has be extended in view of enlarging the class of data sets to be treated.The core idea of phase space reconstruction was inspired by the analysis of deterministic chaotic data.In contrast to many expectations,purely deterministicand low-dimensional data are rare,and most data from?eld measurements are evidently of different nature.Hence,it was an effort of our scienti?c work over the past years,and it was a guiding concept for the revision of this book,to explore thepossibilities to treat other than purely deterministic data sets.xiiiMore informationxiv Preface to the second editionThere is a whole new chapter on non-stationary time series.While detectingnon-stationarity is still brie?y discussed early on in the book,methods to deal with manifestly non-stationary sequences are described in some detail in the second part.As an illustration,a data source of lasting interest,human speech,is used. Also,a new chapter deals with concepts of synchrony between systems,linear and nonlinear correlations,information transfer,and phase synchronisation.Recent attempts on modelling nonlinear stochastic processes are discussed in Chapter12.The theoretical framework for?tting Fokker–Planck equations to data will be reviewed and evaluated.While Chapter9presents some progress that has been made in modelling input–output systems with stochastic but observed input and on the embedding of time delayed feedback systems,the chapter on mod-elling considers a data driven phase space approach towards Markov chains.Wind speed measurements are used as data which are best considered to be of nonlinear stochastic nature despite the fact that a physically adequate mathematical model is the deterministic Navier–Stokes equation.In the chapter on invariant quantities,new material on entropy has been included, mainly on the -and continuous entropies.Estimation problems for stochastic ver-sus deterministic data and data with multiple length and time scales are discussed. Since more and more experiments now yield good multivariate data,alternativesto time delay embedding using multiple probe measurements are considered at var-ious places in the text.This new development is also re?ected in the functionalityof the TISEAN programs.A new multivariate data set from a nonlinear semicon-ductor electronic circuit is introduced and used in several places.In particular,a differential equation has been successfully established for this system by analysing the data set.Among other smaller rearrangements,the material from the former chapter “Other selected topics”,has been relocated to places in the text where a connection can be made more naturally.High dimensional and spatio-temporal data is now dis-cussed in the context of embedding.We discuss multi-scale and self-similar signals now in a more appropriate way right after fractal sets,and include recent techniques to analyse power law correlations,for example detrended?uctuation analysis.Of course,many new publications have appeared since1997which are potentiallyrelevant to the scope of this book.At least two new monographs are concerned withthe same topic and a number of review articles.The bibliography has been updatedbut remains a selection not unaffected by personal preferences.We hope that the extended book will prove its usefulness in many applicationsof the methods and further stimulate the?eld of time series analysis.DresdenDecember2002More informationAcknowledgementsIf there is any feature of this book that we are proud of,it is the fact that almost allthe methods are illustrated with real,experimental data.However,this is anythingbut our own achievement–we exploited other people’s work.Thus we are deeplyindebted to the experimental groups who supplied data sets and granted permissionto use them in this book.The production of every one of these data sets requiredskills,experience,and equipment that we ourselves do not have,not forgetting thehours and hours of work spent in the laboratory.We appreciate the generosity ofthe following experimental groups:NMR laser.Our contact persons at the Institute for Physics at Z¨u rich University were Leci Flepp and Joe Simonet;the head of the experimental group is E.Brun.(See AppendixB.2.)Vibrating string.Data were provided by Tim Molteno and Nick Tu?llaro,Otago University, Dunedin,New Zealand.(See Appendix B.3.)Taylor–Couette?ow.The experiment was carried out at the Institute for Applied Physics at Kiel University by Thorsten Buzug and Gerd P?ster.(See Appendix B.4.) Atrial?brillation.This data set is taken from the MIT-BIH Arrhythmia Database,collected by G.B.Moody and R.Mark at Beth Israel Hospital in Boston.(See Appendix B.6.) Human ECG.The ECG recordings we used were taken by Petr Saparin at Saratov State University.(See Appendix B.7.)Foetal ECG.We used noninvasively recorded(human)foetal ECGs taken by John F.Hofmeister as the Department of Obstetrics and Gynecology,University of Colorado,Denver CO.(See Appendix B.7.)Phonation data.This data set was made available by Hanspeter Herzel at the Technical University in Berlin.(See Appendix B.8.)Human posture data.The time series was provided by Steven Boker and Bennett Bertenthal at the Department of Psychology,University of Virginia,Charlottesville V A.(SeeAppendix B.9.)xvMore informationxvi AcknowledgementsAutonomous CO2laser with feedback.The data were taken by Riccardo Meucci and Marco Cio?ni at the INO in Firenze,Italy. (See Appendix B.10.)Nonlinear electric resonance circuit.The experiment was designed and operated by M.Diestelhorst at the University of Halle,Germany.(See Appendix B.11.)Nd:YAG laser.The data we use were recorded in the University of Oldenburg,where we wish to thank Achim Kittel,Falk Lange,Tobias Letz,and J¨u rgen Parisi.(See AppendixB.12.)We used the following data sets published for the Santa Fe Institute Time SeriesContest,which was organised by Neil Gershenfeld and Andreas Weigend in1991:NH3laser.We used data set A and its continuation,which was published after the contest was closed.The data was supplied by U.H¨u bner,N.B.Abraham,and C.O.Weiss.(SeeAppendix B.1.)Human breath rate.The data we used is part of data set B of the contest.It was submitted by Ari Goldberger and coworkers. (See Appendix B.5.)During the composition of the text we asked various people to read all or part of themanuscript.The responses ranged from general encouragement to detailed technicalcomments.In particular we thank Peter Grassberger,James Theiler,Daniel Kaplan,Ulrich Parlitz,and Martin Wiesenfeld for their helpful remarks.Members of ourresearch groups who either contributed by joint work to our experience and knowl-edge or who volunteered to check the correctness of the text are Rainer Hegger,Andreas Schmitz,Marcus Richter,Mario Ragwitz,Frank Schm¨u ser,RathinaswamyBhavanan Govindan,and Sharon Sessions.We have also considerably pro?ted fromcomments and remarks of the readers of the?rst edition of the book.Their effortin writing to us is gratefully appreciated.Last but not least we acknowledge the encouragement and support by SimonCapelin from Cambridge University Press and the excellent help in questions ofstyle and English grammar by Sheila Shepherd.。
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Felix Otto
models considered were of gradient-flow type and thus endowed with a variational interpretation: steepest descent in a multiscale energy landscape. The examples are • The branching of domains in uniaxial ferromagnets [1] (with R. Choksi and R. V. Kohn). Strongly uniaxial ferromagnets have only two favored magnetization directions (“up” and “down”). The width of the corresponding domains decreases towards a sample surface perpendicular to the favored axis. We rigorously establish the scaling of the energy in the sample dimensions in support of this behavior. To leading order, the micromagnetic model behaves like a three-dimensional analogue of the Kohn-M¨ uller [10] model for twin branching. • The period of cross-tie walls in ferromagnetic films [2] (with A. DeSimone, R. V. Kohn and S. M¨ uller). Cross-tie walls are transition layers between domains in ferromagnetic films. They display a periodic structure in the tangential direction. The experimentally observed scaling of the period in the material parameters is not well-understood [9]. In this paper, we present a combination of heuristic and rigorous analysis which reproduces the experimental scaling and thus identifies the relevant mechanism. • The rate of capillarity-driven spreading of a thin droplet [6] (with L. Giacomelli). Here, the starting point is the lubrication approximation. The scale invariant version of the model is ill-posed and has to be regularized near the contact line, e. g. through allowing finite slippage. In this paper, we rigorously derive a scaling law for the spreading of the droplet in an intermediate time regime. This scaling law depends only logarithmically on the length scale introduce agreement with a conjecture of de Gennes [5]. • The rate of coarsening in spinodal decomposition [11] (with R. V. Kohn). Spinodal decomposition is usually modelled by a Cahn-Hilliard equation. In the later stages, it is experimentally observed that the phase distribution coarsens in a statistically self-similar fashion. In this paper, we rigorously prove upper bounds for this coarsening process. The exponents are the ones heuristically expected and depend on whether the mobility is degenerate or non-degenerate: t1/4 resp. t1/3 . In [3], we predict a cross-over for almost degenerate mobility due to a change in the coarsening mechanism. • The first-order correction to the Lifshitz-Slyozov-Wagner theory for Ostwald ripening [7] (with A. H¨ onig and B. Niethammer). Ostwald ripening describes the late stage of spinodal decomposition in an off-critical mixture (volume fraction of one phase φ ≪ 1). The minority phase then consists of several particles immersed in a matrix of the majority phase. The particles are approximately spherical and don’t move—the Lifshitz–Slyozov—Wagner theory describes the evolution of the radii distribution. There is a major interest in identifying the next-order correction term in φ. We rigorously show that there is a cross-over in the correction term from φ1/3 to φ1/2 depending on the system size. Our method to rigorously analyze these scaling laws in a multiscale model is based on relating integral quantities (energies, average length scales, dissipation rates...). It is different from the more local method of matched asymptotic expan-
ICM 2002 · Vol. III · 1–3
Cross-over in Scaling Laws: A Simple Example from Micromagnetics
arXiv:math-ph/0305001v1 1 May 2003
Felix Otto∗
Abstract Scaling laws for characteristic length scales (in time or in the model parameters) are both experimentally robust and accessible for rigorous analysis. In multiscale situations cross–overs between different scaling laws are observed. We give a simple example from micromagnetics. In soft ferromagnetic films, the geometric character of a wall separating two magnetic domains depends on the film thickness. We identify this transition from a N´ eel wall to an Asymmetric Bloch wall by rigorously establishing a cross–over in the specific wall energy.
1. Introduction
Many continuum systems in materials science display pattern formation. These patterns are characterized by one or several length scales. The scaling of these characteristic lengths in the material parameters and/or in time are usually an experimentally robust feature. These scaling laws, and their characterizing exponents, are of interest to theoretical physics since they express a certain universality. At the same time, scaling laws (rather than more detailed features) are ameanable to heuristic and rigorous analysis and thus are a good test for the model and a challenge for mathematics. Scaling laws and their exponents reflect a scale invariance. In a multiscale model, these scale invariances are broken and only approximately valid in certain parameter and/or time regimes. The cross-over between two scaling laws reflects a change in the dominant physical mechanisms. In studying cross-overs, theoretical analysis may have an advantage over numerical simulation which has to explore many parameter decades and thus has to cope with widely separated length scales. Together with various collaborators, the author has analyzed scaling laws and their cross-overs in both static (variational) and dynamic models. The dynamic