统计学CH06 英文版
统计学要点摘要英文版-Statistic-Review培训讲学
Chapter 2 Statistic ReviewA. Random variables; 1. expected value:Define : X is a discrete random variable, “ the mean (or expected value) of X ” is theweighted average of the possible outcomes, where the probabilities of the outcome serve as the appropriate weight.∑++===n n i i X X p X p X p X p X E ......)(2211μ p i is ith of prob., i=1,2, ……nInterpretation : The random variable is a variable that have a probability associated witheach outcome. Outcome is not controlled. Discrete random Var. : has finite outcome, or outcome is countable infinite.Continuous random Var.: uncountable infinite outcome, the probability of each outcome issmall because of too many numbers.For normal random Var., probability density function is used to calculate the probabilitybetween the are.E( ): the expectations operator, ∑=1i p→ NXobs of N X X X X X Ni iN∑==+++=`1321.)(....… “ sample mean ”, used to estimate X μThe X ϖis changed from sample to sample. is not a fixed on time, the outcome selected should not be the same. There is prob. associated with each X ϖ. X ϖis also a random variable, we can calculate E(X ϖ).2. variance: measure the dispersion(分散), the range of the value∑-=-=-==2222)()]([})]([{)(Xi i i i XX E X E X E X E X p X Var μσ “population variance ” constant)(X Var X =σ …………... “population standard deviation ”→ 1)(ˆ222--==∑N X X S iX X σ“sample variance ” used to estimate 2X σ2ˆX X X S S ==σ……….. “sample standard deviation ”3. joint distribution: (linear relation of X and Y bi-variance random variance.))])([(),(Y X Y X E Y X Cov μμ--=Covariance, measuring the linear relationship between X and Y.),(Y X Cov , depends on the units of X and Y; different unit-> different),(Y X Cov >0: the best-fitting line has a positive slope, positive relationship between X and Y . ),(Y X Cov <0: the best-fitting line has a negative slope, negative relationship between X and Y . ),(Y X Cov =0: there is no linear relationship between X and Y , but may be have nonlinearrelationship.YX XY Y X σσρ),cov(=“population correlation coefficient ” is scale free.XY ρ>0, a positive correction, XY ρ<0, a negative correction XY ρ=0, no linear relationship between X and Y11-≥≥XY ρ, XY ρ=1: regression line is a straight line with positive slope, XY ρ=-1: regressionline is a straight line with negative slope.1))((),(ˆ1---=∑=N Y Y X XY X vCo Ni i i,YX XYS S Y X ),(v ˆco =γ=221)()())((∑∑∑----=Y YX X Y Y X XiiNi i i“sample correlation coefficient ”)int (,1,1)(,1)(11prob jo pY p X p ijN i N j ij===∑∑∑∑==E(X) =0.263×1+0.403×2+0.334×3=2.071 Var(X) =4.881-(2.071)2=0.591959 E(Y) =0.298×6+0.385×5+0317×4=4.981 Var(Y)= 25.425-(4.981)2=0.614639Cov(X,Y)=)()(Y E X E Y X p ijj i j i -∑∑=10.001-2.071×4.981= -0.3174. formulaE(b)=b , Var(b)=0；E(aX)=aE(X), Var(aX)=a 2 Var(X )； E(aX+b)=aE(X)+b, Var(aX+b)= a 2 Var(X ))()]([)]}([{])([)]()[()(2222222X Var a X E X E a X E X a E b X aE b ax E b aX E b aX E b aX Var =-=-=+-+=+-+=+ΘE(X+Y)=E(X)+E(Y), Var(X+Y)=Var(X)+Var(Y)+2Cov(X, Y)\),(2)()()]}()][([2)]([)]({[)]}([)]({[)]()([)]()[()(22222Y X Cov Y Var X Var Y E Y X E X Y E Y X E X E Y E Y X E X E Y E X E Y X E Y X E Y X E Y X Var ++=----+-=-+-=--+=+-+=+Θ If X and Y is independent (linear uncorrelated), than E(X+Y)=E(X)+E(Y) → Cov(X,Y)=0, XY ρ=0, → Var(X+Y)=Var(X)+Var(Y))(][)]()()()([)]()][([),(=-=-=+--=+--=--=Y X Y X Y X Y X Y X XY E X Y XY E Y E X E Y XE X XE XY E Y E Y X E X E Y X Cov μμμμμμμμμμΘ,0),(=Y X Cov can ’t define the X and Y are independent. 2))(()(X E X X E ≠• X is not independent of itself.B. (probability) distributions:1. the normal distribution:),(`~2X X N X σμ2. the standard normal distribution:)1,0(~,N Z X Z XXσμ-=3. the Chi-square distribution:∑=+++==Ni N i NZ Z Z Z 12222122Λχ Z i : N independently normal distribution random variables with 0 mean and variance 1. → As N gets larger, the χ2 distribution because an approximation of normal distribution. the rang of χ2 : 0 → ∞ 4. the t distribution:If (1) Z is normal distribution with mean 0 and variance 1, (2) χ2 is distribution as Chi-square with N degrees of freedom, (3) Z and χ2 are independent Thenn nt NZ~2χ→As N gets large, the t distribution will tend to approximately be normal distribution. 5. the F distribution:if X and Y are independent and distribution as Chi-square will N1 and N2 degrees of freedom, respectively thus21,21~N N F N YN Xassume Xi ’s are independent each other,),(~2NX XX σμ regardless of the distribution of XE(X)=)(1)(1][21N i iX X X E NX E N NX E +++==∑∑Λ =X X X X X NN X X N μμμ=•=+++)(1Λ N N N X N X NX X X i i X22222)(1)var(1)1var()var(σσσ=•====∑∑22121)]()[()var(N N i X X X E X X X E X +++-+++=∑ΛΛΘ =22211)]}([)([)]({[N N X E X X E X X E X E -++-+-Λ =∑∑--+-)]()][([2)]([2j j i i i i X E X X E X E X E X =)var()var()var()var(21N i X X X X +++=∑Λ=2222X X X X N σσσσ=+++Λ If ),(~2XX N X σμ, then i.i.d. (independent with other variables, identically distributed over time) If ),(~2XX N X σμ, then ),(~2NN X XX σμ＊ Central limit theorem:If ),(~2XX X σμ (at any distribution), then ),(~2NN X XX σμ as N(sample size) increase.1)(ˆ2--==∑N X XS iX σ, what is the distribution of NS X 0μ- ?C. hypothesis testing:ex. H 0: μ=100… null hypothesis, H 1: μ≠100 …alternative hypothesis α=0.05 … level of significanceto get the value of X , the test statistic, NX Z 2100σ-=if σ2 is know.To check the critical value, Zc ( whether or not to accept H 0)If accept H 0:“we can not reject H 0 at 95% confidence level based on the data we have ”If reject H 0:“we can reject H 0 at 5% level of confidence.”Type I error: reject H 0 when H 0 is true, the probability of making type I error is α.Type II error: accept H 0 when H 0 is false, the probability of making type II error is difficult to determine.When the confidence interval increase, then the type I error will reduce and type II error will increase.※ 22000)1()1()()(σσμμμ---=-=-N S N NX S NX NS X Θ(1) if ),(~2XX N X σμ, then ),(~2NN X XX σμ, the numerator(分子) (NS X 0μ-) is the standardnormal variable Z, and22)1(σS N - is 21-N χ(21222222)()1()(11-=-=-⇒--=∑∑N i i X X S N X X N S χσσσσ)1210~)1(~----∴N N t N ZNS X χμ, if numerator and denominator(分母) are independent.(2) if ),(~2XX N X σμ, the we will appeal to the central limit theorem.D. point and interval estimate:1. point estimate: ex X =40 , but we don ’t know whether the true value approximates it ornot.2. interval estimate:NZ X Xσα2±, α=0.05 , if X σ is know.NS t X Xt 1,2-±α,α=0.05 , if X σ is unknown and N is small (<31).95% interval will include the true value.E. properties of estimator: unbiasedness, efficiency, consistency1. unbiasedness:βˆ is an unbiased estimator of β, if E(βˆ) =β(true value) → ex. X is an unbiased estimator of X μ；ex. 2X S is an unbiased estimator of 2X σ]})()([11{]1)([)(2222∑∑----=--=X X X N X N E N X X E SE μμ =222)(11X X X NN N N σσσ=-- ∑∑∑----+-=---=-)])((2)()[()]()[()(2222X X X X X X X X X X X X X X μμμμμμΘ =∑∑----+-)()(2)()(22X X X X X X X N X μμμμ =222)(2)()(X X X X N X N X μμμ---+-∑ =22)()(X X X N X μμ---∑→ bias= E(βˆ) －β2. efficiency:define: (a) minimum mean square error,MSE(βˆ)=E(βˆ-β)2 =[bias(βˆ)]2+Var(βˆ).Pove : 22]})([)](ˆ{[)ˆ(ββββββ-+-=-E E E E =]})([)](ˆ{[2])([)](ˆ[22ββββββββ--+-+-E E E E E E =)}ˆ()]ˆ([ˆ)ˆ(ˆ{2)]ˆ([)ˆvar(22βββββββββE E E E bias +--++ =0)]ˆ([)ˆvar(2++ββbias → we check X , it is mostly around μ.→ when bias(βˆ)=0, then MSE(βˆ)=Var(βˆ) → If we have an unbiased estimator with large dispersion of true value (ie. High var.) and abiased estimator with low var. we might prefer biased estimator than unbiased estimator to maximize the precision of prediction.Def. (b) If for a given sample size, (1) βˆis unbiased, (2) Var(βˆ))~var(β≤, when β~ is any other unbiased estimator of β. Then βˆ is an efficient estimator of β. Cramer-Rao lower bounds: gives a lower limit for the variance of any unbiased estimator.EX. The lower var(X )=N 2σ ; the lower variance for N 422σσ=( between the all unbiased estimators, the min. is kN -=422)ˆvar(σσ)3. consistency:The probability limit of βˆ, plim (βˆ)=β as N →∞ ; ,1)ˆ(lim =<-∞→δββprob N for any δ>0. 0)ˆ(,1)ˆ(:=>-==∞→∞→δββββN N prob prob ie2. criteria to consistency:(a) sufficient condition for consistency: not necessary for consistencyβˆ is a consistent estimator of β if plim (βˆ)=β as N →∞ → consistency ⇒⨯ plim (βˆ)=β (b) mean square error consistency:If βˆ is an estimator of β and if ,0)ˆ(lim =∞→βMSE N then βˆ is a consistent estimator of β→ ie X is a consistent estimator of X μ0lim ,0)var()]([)(222==+=+=∞→MSE NNX X bias X MSE N XXσσΘSlutsky ’s theorem: If plim (βˆ)=β and g(βˆ) is a continuous function of βˆ, then plim g(βˆ)=g[ plim (βˆ)] ( This is “ consistency carries over ” However, consistency is not always carries over. ) Biasedness doesn ’t carry over.4. asymptotic unbiasedness: as N becomes large and large.“βˆ” is an asymptotically unbiased estimator of β if ,)ˆ(lim ββ=∞→E N → if estimator is unbiased ,⇐⨯⇒ it will be asym. unbiased.→ Ex. If ∑-=22)(1~X XNiσ, it is asym. unbias221)~(σσNN E -=Θ, 22)~(lim σσ=∞→E N5. asymptotic efficiency: N →∞“βˆ” is an asymptotically efficient estimator of β if all of the following conditions are satisfied:(a) βˆ is an asymptotic distribution with finite mean and finite variance. (b) βˆ is consistent ; (c) no other consistent estimator of β has small asymptotic variance than βˆ. → if estimator is efficient, ⇐⨯⇒it will be asym. efficient.unbiaedness efficiency consistencyasym. unbiasedness asym. efficiencybiaedness inefficiency consistencyasym. unbiasedness asym. efficiencybiaedness inefficiency inconsistencyasym. unbiasedness asym. inefficiencyReview of linear algebra1. a mrtrix A is idempotent iff AA=A.2. If the inner product of the 2 vectors vanishes (ie., the scalar is 0), then the vector are orthogonal. [An inner product (or scalar, or dot product) of 2 vectors is a row vector times a column vector, yielding a scalar. An outer product of 2 vectors is a column vector times a new vector, yielding a matrix ]3. The rank of any matrix A, ρ(A), is the size of the largest n on-vanishing determinant contained in A;Or, the rank is the maximum number of linearly independent rows or columns of A, where a 1, a 2, … a N is a set of linearly independent vectors, iff∑==Nj j ja k10 ,necessarily implies021====N k k k Λ→Ex. A A ,6832⎥⎦⎤⎢⎣⎡==12-24=-12≠ 0, =∴)(A ρ 2B B ,4263⎥⎦⎤⎢⎣⎡==12-12=0 , =∴)(B ρ 1→ properties:a. )(0A ρ≤=intege r ≦min(M,N) where A is an M ×N matrix.b. )(A ρ=N, 0)0(=ρc. )(A ρ=)'()'()'(AA A A A ρρρ==d. if A and B are of the same order, )()()(B A B A ρρρ+≤+.e. if AB is defined, )](),([min )(B A AB ρρρ≤f. If A is diagonal, )(A ρ= number of nonzero elements.g. If A is idempotent, )(A ρ=trace(A.)h. The ranks of a matrix is not changed if one row ( or column) is multiplied by a nonzero constant, or if such a multiple of one row (column) is added to another row (column).4. A aquare matrix of order N is nonsingular iff it is of full rank, )(A ρ=N (or ≠A 0); otherwise, it is aingular. The rank of matrix is unchanged by premultiplying or postmultiplying by a nonsingular matrix.5. differentiation in matrix notation (rules):a. If ⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=⨯⨯M M M M M a a a X X X M M 111, where a i =(i=1,2,…M) are constant, then=∂∂XX a )'( a. b. IF A is a symmetric matrix of order M×M where the typical element is a constant a ij , then=∂∂X AX X )'(2AX, if not a symmetric matrix =∂∂XAX X )'((A ’+A)X. c. IF A is a symmetric matrix of order M×M where the typical element is a constant a ij , then=∂∂∂')'(X X AX X 2A, =∂∂AAX X )'(XX ’If Y and Z are vectors, B is a matrix, then=∂∂Y BZ Y )'(BZ , =∂∂Z BZ Y )'(B ’Y , =∂∂BBZ Y )'(YZ ’Formula for Matrix(A ’)’=NM A ⨯ =⨯⨯)'(KN N M B A B ’A ’A B ≠BA in general'')'(C A C A NM ±=±⨯111)(---⨯⨯=D E E D MM M M , iff det D≠0 and E≠0 ( ie. Iff D and E nonsingular matrices)(D -1)’= (D’)-1 Det D=det D ’Trace (D ± E )= trace (D) ±trace (E) Trace )()(NN MN N M FA trace F A ⨯⨯⨯=Trace (scalar)=saclar E[trace (D)]=trace (E(D))。
统计学原理英文版review
Key Words1、Nominal data 列名数据（定类数据）2、Ordinal data 顺序数据（定序数据）3、Interval data 间隔数据（定距数据）4、Ratio data 比率数据（定比数据）5、Descriptive statistics 描述统计学6、inferential statistics 推断统计学7、Elementary units 基本单位8、Population 总体9、Qualitative variable 定性变量10、quantitative variable 定量变量11、Sample survey 抽样调查12、Census （人口）普查13、Probability sample 概率抽样14、nonprobability sample 非概率抽样15、Outliers 异常值16、Bar chart 柱状图17、Pie chart 饼图18、Scatter diagram 散点图19、Histogram 直方图20、Class mark 组内代表值21、class width 组距22、Frequency 频数23、Open-ended class 开口组24、Per capita GDP 人均GDP25、Arithmetic mean 算术平均数26、Geometric mean 几何平均数27、Range 极差，全距28、interquartile range 四分位差，内距29、Median 中位数30、Mode 众数31、Parameter 参数32、Statistic 统计量33、Variance 方差34、standard deviation 标准差35、Mean absolute deviation 平均差36、Skewness 偏度37、Kurtosis 峰度38、Coefficient of variation 离散系数39、stock price indexes 股票价格指数40、consumer price index (CPI) 消费价格指数41、simple index 个体指数42、combined index 总指数43、Aggregative index number 综合指数44、Average index number 平均数指数45、intermediate factor 同度量因素（媒介因素）46、Laspeyres indexes 拉氏指数47、Paasche indexes 派氏指数48、Fisher’s ideal indexes 费雪理想指数49、Simple Linear Correlation 简单线性相关50、Regression 回归51、Residuals 残差52、Coefficient of Correlation 相关系数53、Ordinary Least Squares 最小二乘法54、Independent variable 自变量55、Dependent variable 因变量56、Slope 斜率57、Intercept 截距58、Coefficient of Determination 决定系数59、time series 时间序列60、Long-term trend 长期趋势61、Seasonal fluctuation 季节变动62、Moving averages method 移动平均法63、seasonal indexes 季节指数Functions :1)Countif (range, criteria)用来计算区域中满足给定条件的单元格的个数。
双语统计学——精选推荐
双语统计学Example 13.1 ...that high-fiber cereals reduce the likelihood of various forms of cancer. ...another advantage of eating their product —potential weight reduction for dieters. Can the scientist conclude at the 5% significance level that his belief is correct? (p396)Solution : The data are obviously interval. This problem objective/data typecombination tells us that the parameter to be tested is the difference between two means, µ1 – µ2. Then, the test is known as the unequal-variance t-test.I. Formulate the Hypotheses H0: µ1 ≥ µ2; H1: µ1 ＜ µ2.II. Select the Test StatisticIII. Identify the Rejection RegionSelect the significance level α= 0.05, referring to the t-value table, and a critical value t α(ν) = t0.05(123) = 1.658. Hence, the rejection region is: t ＜-1.658 .IV . Make the Statistical Decision On the sample data, the value of the test statistic t is:and it does fall into the rejection region, then we reject H0 .As a result, we conclude that there is sufficient evidence to infer that consumers of high-fiber cereal do eat fewer calories at lunch than do nonconsumers.Example 13.4 .... Matched pairs experiment depending on GPA.(p416)Solution : The experimental design tells us that the parameter of interest is the mean ofthe population of differences, which we label µD. Note that µ1 – µ2 = µD but that we test µD because of the way the experiment was performed. ? I. Formulate the Hypotheses H0: µD= 0; H1: µD ＞ 0II. Select the Test StatisticIII. Identify the Rejection Region ......VI. Make the Statistical Decision .....Example 15.1 ... The marketing manager has to decide how to market the new product. She can create advertising that emphasizes convenience, quality, or price. T o facilitate a decision, she conducts an experiment in three different small cities. ...()1212604.02633.2302.09x x t µµ-----===-()1212xx t µµ---=()()()2221122222211221211sn sn s n s n n n ν+=?+--??t =Solution : According to the conditions in the problem, we use ANOV A test. It is knownthat the dependent variable (response variable) is weekly sales (packages), and the factor is the advertising strategy and there are three levels. ? I. Formulate the HypothesesH0: µ1=µ2=µ3 ; H1: At least two means differ.II. Select the Test StatisticMST = SST / dfT , MSE= SSE/ dfE ,dfT = k – 1, dfE = n - kIII. Identify the Rejection RegionSelect the significance level α=0.05, referring to the F-value table, and a critical value F α(dfT , dfE )= F 0.05(2, 57)= 3.15. Hence, the rejection region is: F ＞ 3.15.IV . Make the Statistical DecisionAt first, we calculate the sum of squares, SS:According to the sample data,ΣΣxij = 36,784 ΣΣx2ij = 23,115,540; T.1 = 11,551, T.2 = 13,060, T.3 = 12,173, ΣT2.j = 452,171,130. Then,SST = 57,513, SSE = 506,983, SS(total)= 564,496.Thus, MST= SST / dfT = 28,756, MSE = 8,894, and F = MST / MSE = 3.23.When α = 0.05, we can reject H0 . So, there is enough evidence to infer that the mean weekly sales differ between the three cities.V . Tabulate ANOVASource of V ariation Sum of Squares df MS F-StatisticTreatments 57,513 2 28,756 3.23 error 506,983 57 8,894 total 564,495 59()~,TEM STF F df df M SE=22()/ijijijijSS total xxrk ??=-∑∑∑∑22.1/j ij jijSST T x rk r ??=-∑∑∑()SSE SS total SST =-15.2 ... cholesterol, which can lead to heart attacks. ... Four such drugs. ...Solution : According to the conditions in the problem, we use ANOV A test. It is known that we identify the experimental design as randomized block, and response variable is the cholesterol reduction, the treatments are the drugs, and the blocks are the 25 similar groups of men.I. Formulate the HypothesesH0: µ1=µ2=µ3=µ4 ; H1: At least two means differ. II. Select the Test StatisticMST = SST / dfT , MSE= SSE/ dfE ,dfT = k – 1, dfE = n – k – b + 1III. Identify the Rejection RegionSelect the significance level α=0.05, referring to the F-value table, and a critical value F α(dfT, dfE)= F0.05(3, 72)= 2.76. Hence, the rejection region is: F ＞ 2.76.IV . Make the Statistical Decision At first, we calculate the sum of squares, SS:According to the sample data,2i.139,340.87iT =∑2·j779,562.89jT =∑()~,T E M STF F df df M SE=22()/1111ij k b k b SS Total x x bkijj i j i ??=- ====∑∑∑∑2·j 21/111ij k k b SST T x bkbj j i ?? ?=- ?===??∑∑∑2.21/111i ij b k b SSB T x bk ki j i ?? ?=- ?===??∑∑∑()SSE SS Total SST SSB =--1,760.3ijjix =∑∑236,173.73ijj ix =∑∑SS(Total) = 5,187.2, SST = 196.0SSB = 3,848.7 SSE = 1,142.5MST = 65.3 MSE = 15.9So, F = MST / MSE = 65.3 / 15.9 = 4.1 .When α=0.05, we do reject H0 .V. Tabulate ANOVAV ariation Source SS df MS F λTreatment T 196.0 3 65.3 4.1 2.76Block B 3,848.7 24 160.3 10.1 1.67Error 1,142.5 72 15.9Total 5,187.2 99A Type I error occurs when you conclude that difference exist when, in fact, they do not . A Type II error is committed when the test reveals no difference when at least two means differ. ... Because the p-value = 0.0094, we conclude that there is sufficient evidence to infer that at least two of the drugs differ。
统计学专业英语词汇完整版
Breakdownbound,崩溃界/崩溃点
C
Canonicalcorrelation,典型相关
Caption,纵标目
Case-controlstudy,病例对照研究
Categoricalvariable,分类变量
Catenary,悬链线
Cauchydistribution,柯西分布
Confidencelowerlimit,置信下限
Confidenceupperlimit,置信上限
ConfirmatoryFactorAnalysis,验证性因子分析
Confirmatoryresearch,证实性实验研究
Confoundingfactor,混杂因素
Conjoint,联合分析
Consistency,相合性
Clusteranalysis,聚类分析
Clustersampling,整群抽样
Code,代码
Codeddata,编码数据
Coding,编码
Coefficientofcontingency,列联系数
Coefficientofdetermination,决定系数
Coefficientofmultiplecorrelation,多重相关系数
Datahandling,数据处理
Datamanipulation,数据处理
Dataprocessing,数据处理
Datareduction,数据缩减
Dataset,数据集
Datasources,数据来源
Datatransformation,数据变换
Datavalidity,数据有效性
Data-in,数据输入
Compassion,伸缩
基础统计学简介英文版
• KnowledБайду номын сангаасe of statistical methods at least helps you understand why decisions are made
– In future you will make decisions that involve data
1-6 6
Types of Statistics
Descriptive Statistics: Methods of organizing, summarizing, and presenting data in an informative way.
EXAMPLE 1: A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.
1-4 4
What is Meant by Statistics?
In common usage statistics refers to numerical information….. But in this course the term has a wider meaning….
Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.
英文商务统计学ppt_第六章Ch06
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc..
Translation to the Standardized Normal Distribution
Translate from X to the standardized normal (the “Z” distribution) by subtracting the mean of X and dividing by its standard deviation:
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.. Chap 6-6
The Normal Distribution Shape
f(X) Changing μ shifts the distribution left or right. Changing σ increases or decreases the spread.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc..
Chap 6-2
Continuous Probability Distributions
A continuous random variable is a variable that can assume any value on a continuum (can assume an uncountable number of values)
X = any value of the continuous variable
统计学英文
bsolute 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。
统计学英文版教材课件
Combining Events
There are some important ways in which events can be combined that we will encounter repeatedly throughout this course. Suppose we have two events, A and B .
For example, A ∪ B = {1, 3, 4, 5}.
S A 1 5 2
STAT7055 - Lecture 2
B 3 4
6
February 17, 2016 13 / 64
Introduction
Intersection, Union and Complement
Complement
STAT7055 - Lecture 2
February 17, 2016
3 / 64
Introduction
Definitions
Probabilities of Outcomes
The probability of an outcome occurring on a single trial is written as P (Oi ). Probabilities associated with the outcomes in a sample space must satisfy two important requirements:
STAT7055 - Lecture 2
February 17, 2016
7 / 64
Introduction
Events
Events
A simple event is an individual outcome from the sample space. An event is a collection of one or more simple events (or outcomes).
《统计学》_各章关键术语（中英文对照）
《统计学》_各章关键术语（中英文对照）第二部分各章关键术语（中英文对照）第1章统计学（statistics）随机性（randomness）描述统计学（descriptive statistics）推断统计学（inferential statistics）总体（population）母体（parent）(parent population）样本、子样（sample）调查对象总体（respondents population）有限总体（finite population）调查的理论总体（survey’s heoretical population）超总体（super population）变量（variable）数据（data）原始数据（original data）派生数据（derived data）定类尺度（nominal scale）定类尺度变量（nominal scale level variable）定类尺度数据（nominal scale level data）定序尺度（ordinal scale）定序尺度变量（ordinal scale level variable）定序尺度数据（ordinal scale level data）定距尺度（interval scale）定距尺度变量（interval scale level variable）定距尺度数据（interval scale level data）定比尺度（ratio scale）定比尺度变量（ratio scale level variable）定比尺度数据（ratio scale level data）分类变量（categorical variable）定性变量、属性变量（qualitative variable）数值变量（numerical variable）定量变量、数量变量（quantitative variable）绝对数变量（absolute number level variable）绝对数数据（absolute number level data）比率变量（ratio level variable）比率数据（ratio level data）实验数据（experimental data）调查数据（survey data）观察数据（observed data）第2章随机性（randomness）随机现象（random phenomenon）随机试验（random experiment）事件（event）基本事件（elementary event）复合事件（union of event）必然事件（certain event）不可能事件（impossible event）基本事件空间（elementary event space）互不相容事件（mutually exclusive events）统计独立（statistical independent）统计相依（statistical dependence）概率（probability）古典方法概率（classical method probability）相对频数方法概率（relative frequency method probability）主观方法概率（subjective method probability）几何概率（geometric probability）条件概率（conditional probability）全概率公式（formula of total probability）贝叶斯公式（Bay es’ formula）先验概率（prior probability）后验概率（posterior probability）随机变量（random variable）离散型随机变量（discrete type random variable）连续型随机变量（continuous type random variable）概率分布（probability distribution）特征数（characteristic number）位置特征数（location characteristic number）数学期望（mathematical expectation）散布特征数（scatter characteristic number）方差（variance）标准差（standard deviation）变异系数（variable coefficient）贝努里分布（Bernoulli distribution）二点分布（two-point distribution） 0-1分布（zero-one distribution）贝努里试验（Bernoulli trials）二项分布（binomial distribution）超几何分布（hyper-geometric distribution）正态分布（normal distribution）正态概率密度函数（normal probability density function）正态概率密度曲线（normal probability density curve）正态随机变量（normal random variable）卡方分布（chi-square distribution）F_分布（F-distribution）t_分布（t-distribution）“学生”氏t_分布（Student’s t-distribution）列联表（contingency table）联合概率分布（joint probability distribution）边缘概率分布（marginal probability distribution）条件分布（conditional distribution）协方差（covariance）相关系数（correlation coefficient）第3章统计调查（statistical survey）数据收集（collection of data）统计单位（statistical unit）统计个体（statistical individual）社会经济总体（socioeconomic population）调查对象总体（respondents population）有限总体（finite population）标志（character）标志值（character value）属性标志（attributive character ）品质标志（qualitative character ）数量标志（numerical indication）不变标志（invariant indication）变异（variation）调查条目（item of survey）指标（indicator）统计指标（statistical indicator）总量指标（total amount indicator）绝对数（absolute number）统计单位总量（total amount of statistical unit ）标志值总量（total amount of indication value）（total amount of character value）时期性总量指标（time period total amount indicator）流量指标（flow indicator）时点性总量指标（time point total amount indicator）存量指标（stock indicator）平均指标（average indicator）平均数（average number）相对指标（relative indicator）相对数（relative number）动态相对指标（dynamic relative indicator）发展速度（speed of development）增长速度（speed of growth）增长量（growth amount）百分点（percentage point）计划完成相对指标（relative indicator of fulfilling plan）比较相对指标（comparison relative indicator）结构相对指标（structural relative indicator）强度相对指标（intensity relative indicator）基期（base period）报告期（given period）分组（classification）（grouping）统计分组（statistical classification）（statistical grouping）组（class）（group）分组设计（class divisible design）（group divisible design）互斥性（mutually exclusive）包容性（hold）分组标志（classification character）（grouping character）按品质标志分组（classification by qualitative character）（grouping by qualitativecharacter）按数量标志分组（classification by numerical indication）（grouping by numericalindication）离散型分组标志（discrete classification character）（discrete grouping character）连续型分组标志（continuous classification character）（continuous grouping character）单项式分组设计（single-valued class divisible design）（single-valued group divisibledesign）组距式分组设计（class interval divisible design）（group interval divisible design）组界（class boundary）（group boundary）频数（frequency）（frequency number）频率（frequency）组距（class interval）（group interval）组限（class limit）（group limit）下限（lower limit）上限（upper limit）组中值（class mid-value）（group mid-value）开口组（open class）（open-end class）（open-end group）开口式分组（open-end grouping）等距式分组设计（equal class interval divisible design）（equal group interval divisibledesign）不等距分组设计（unequal class interval divisible design）（unequal group interval divisibledesign）调查方案（survey plan）抽样调查（sample survey）有限总体概率抽样（probability sampling in finite populations）抽样单位（sampling unit）个体抽样（elements sampling）等距抽样（systematic sampling）整群抽样（cluster sampling）放回抽样（sampling with replacement）不放回抽样（sampling without replacement）分层抽样（stratified sampling）概率样本（probability sample）样本统计量（sample statistic）估计量（estimator）估计值（estimate）无偏估计量（unbiased estimator）有偏估计量（biased estimator）偏差（bias）精度（degree of precision）估计量的方差（variance of estimates）标准误（standard error）准确度（degree of accuracy）均方误差（mean square error）估计（estimation）点估计（point estimation）区间估计（interval estimate）置信区间（confidence interval）置信下限（confidence lower limit）置信上限（confidence upper limit）置信概率（confidence probability）总体均值（population mean）总体总值（population total）总体比例（population proportion）总体比率（population ratio）简单随机抽样（simple random sampling）简单随机样本（simple random sample）研究域（domains of study）子总体（subpopulations）抽样框（frame）估计量的估计方差（estimated variance of estimates）第4章频数（frequency）（frequency number）频率（frequency）分布列（distribution series）经验分布（empirical distribution）理论分布（theoretical distribution）品质型数据分布列（qualitative data distribution series）数量型数据分布列（quantitative data distribution series）单项式数列（single-valued distribution series）组距式数列（class interval distribution series）频率密度（frequency density）分布棒图（bar graph of distribution）分布直方图（histogram of distribution）分布折线图（polygon of distribution）累积分布数列（cumulative distribution series）累积分布图（polygon of cumulative distribution）位置特征（location characteristic）位置特征数（location characteristic number）平均值、均值（mean）平均数（average number）权数（weight number）加权算术平均数（weighted arithmetic average）加权算术平均值（weighted arithmeticmean）简单算术平均数（simple arithmetic average）简单算术平均值（simple arithmetic mean）加权调和平均数（weighted harmonic average）加权调和平均值（weighted harmonicmean）简单调和平均数（simple harmonic average）简单调和平均值（simple harmonic mean）加权几何平均数（weighted geometric average）加权几何平均值（weighted geometricmean）简单几何平均数（simple geometric average）简单几何平均值（simple geometric mean）绝对数数据（absolute number data）比率类型数据（ratio level data）中位数（median）众数（mode）耐抗性（resistance）散布特征（scatter characteristic）散布特征数（scatter characteristic number）极差、全距（range）四分位差（quartile deviation）四分间距（inter-quartile range）上四分位数（upper quartile）下四分位数（lower quartile）在外截断点（outside cutoffs）平均差（mean deviation）方差（variance）标准差（standard deviation）变异系数（variable coefficient）第5章随机样本（random sample）简单随机样本（simple random sample）参数估计（parameter estimation）矩（moment）矩估计（moment estimation）修正样本方差（modified sample variance）极大似然估计（maximum likelihood estimate）参数空间（space of paramete）似然函数（likelihood function）似然方程（likelihood equation）点估计（point estimation）区间估计（interval estimation）假设检验（test of hypothesis）原假设（null hypothesis）备择假设（alternative hypothesis）检验统计量（statistic for test）观察到的显著水平（observed significance level）显著性检验（test of significance）显著水平标准（critical of significance level）临界值（critical value）拒绝域（rejection region）接受域（acceptance region）临界值检验规则（test regulation by critical value）双尾检验（two-tailed tests）显著水平（significance level）单尾检验（one-tailed tests）第一类错误（first-kind error）第一类错误概率（probability of first-kind error）第二类错误（second-kind error）第二类错误概率（probability of second-kind error）P_值（P_value）P_值检验规则（test regulation by P_value）经典统计学（classical statistics）贝叶斯统计学（Bayesian statistics）第6章方差分析（analysis of variance，ANOVA）方差分析恒等式（analysis of variance identity equation）单因子方差分析（one-factor analysis of variance）双因子方差分析（two-factor analysis of variance）总变差平方和（total variation sum of squares）总平方和SST （total sum of squares）组间变差平方和（among class(group) variation sum of squares），回归平方和SSR（regression sum of squares）组内变差平方和（within variation sum of squares）误差平方和SSE（error sum ofsquares）皮尔逊χ2统计量（Pearson’s chi-statistic）分布拟合（fitting of distrbution）分布拟合检验（test of fitting of distrbution）皮尔逊χ2检验（Pearson’s chi-square test）列联表（contingency table）独立性检验（test of independence）数量变量（quantitative variable）属性变量（qualitative variable）对数线性模型（loglinear model）回归分析（regression analysis）随机项（random term）随机扰动项（random disturbance term）回归系数（regression coefficient）总体一元线性回归模型（population linear regression model with a single regressor）总体多元线性回归模型（population multiple regression model with a single regressor）完全多重共线性（perfect multicollinearity）遗漏变量（omitted variable）遗漏变量偏差（omitted variable bias）面板数据（panel data）面板数据回归（panel data regressions）工具变量（instrumental variable）工具变量回归（instrumental variable regressions）两阶段最小平方估计量（two stage least squares estimator）随机化实验（randomized experiment）准实验（quasi-experiment）自然实验（natural experiment）普通最小平方准则（ordinary least squares criterion）最小平方准则（least squares criterion）普通最小平方（ordinary least squares，OLS）最小平方（least squares）最小平方法（least squares method）第7章简单总体（simple population）复合总体（combined population）个体指数：价比（price relative），量比（quantity relative）总指数（general index）（combined index）统计指数（statistical indices）类指数、组指数（class index）动态指数（dynamic index）比较指数（comparison index）计划完成指数（index of fulfilling plan）数量指标指数（quantitative indicator index）物量指数（quantitative index）（quantity index）（quantum index）质量指标指数（qualitative indicator index）价格指数、物价指数（price index）综合指数（aggregative index）（composite index）拉斯贝尔指数（Laspeyres’ index）派许指数（Paasche’s index）阿斯·杨指数（Arthur Young’s index）马歇尔—埃奇沃斯指数（Marshall-Edgeworth’s index）理想指数（ideal index）加权综合指数（weighted aggregate index）平均指数（average index）加权算术平均指数（weighted arithmetic average index）加权调和平均指数（weighted harmonic average index）因子互换（factor-reversal）购买力平价（purchasing power parity，PPP）环比指数（chain index）定基指数（fixed base index）连环替代因素分析法（factor analysis by chain substitution method）不变结构指数、固定构成指数（index of invariable construction）结构指数、结构影响指数（structural index）第8章截面数据（cross-section data）时序数据（time series data）动态数据（dynamic data）时间数列（time series）发展水平（level of development）基期水平（level of base period）报告期水平（level of given period）平均发展水平（average level of development）序时平均数（chronological average）增长量（growth quantity）平均增长量（average growth amount）发展速度（speed of development）增长速度（speed of growth）增长率（growth rate）环比发展速度（chained speed of development）定基发展速度（fixed base speed of development）环比增长速度（chained growth speed）定基增长速度（fixed base growth speed）平均发展速度（average speed of development）平均增长速度（average speed of growth）平均增长率（average growth rate）算术图（arithmetic chart）半对数图（semilog graph）时间数列散点图（scatter diagram of time series）时间数列折线图（broken line graph of time series）水平型时间数列（horizontal patterns in time series data）趋势型时间数列（trend patterns in time series data）季节型时间数列（season patterns in time series data）趋势—季节型时间数列（trend-season patterns in time series data）一次指数平滑平均数（simple exponential smoothing mean）一次指数平滑法（simple exponential smoothing method）最小平方法（leas square method）最小平方准则（least squares criterion）原资料平均法（average of original data method）季节模型（seasonal model）（seasonal pattern）长期趋势（secular trends）季节变动（变差）（seasonal variation）季节波动（seasonal fluctuations）不规则变动（变差）（erratic variation）不规则波动（random fluctuations）时间数列加法模型（additive model of time series）时间数列乘法模型（multiplicative model of time series）。
关于统计学的英文介绍
关于统计学的英文介绍【中英文版】Introduction to StatisticsStatistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It plays a crucial role in various fields, including economics, biology, psychology, and many more. By utilizing statistical methods, we can draw meaningful conclusions and make informed decisions based on the information extracted from the data.统计学是一门研究数据的收集、分析、解释、呈现和组织方法的数学分支。
它在经济学、生物学、心理学等多个领域发挥着至关重要的作用。
通过运用统计方法，我们可以从数据中提取有意义的信息，并据此做出明智的决策。
The beauty of statistics lies in its ability to simplify complex phenomena into quantifiable measures, enabling us to understand patterns, trends, and relationships within the data. Fundamental concepts such as mean, median, and mode help us summarize and describe data, while techniques like hypothesis testing and regression analysis allow us to make predictions and draw inferences.统计学的魅力在于它能将复杂的现象简化为可量化的指标，使我们能够理解数据中的模式、趋势和关系。
统计学英文版
Frequency Table A frequency table is a listing of possible values for a variable, together with the number of observations and/or relative frequencies for each value.
In the 2008 General Social Survey, 2020 respondents answered the question, "How many children have you ever had?" The results were
Graphs for categorical data: bar graphs and pie charts
Bar graphs are called Pareto Charts when the categories are ordered by their frequency, from the tallest bar to the shortest bar
Graphs for quantitative data: dot plot Shows a dot for each subject (observation) placed above its value on a number line. To construct a dot plot
Example Table classifies the 630 parliamentary seats of the Italian chamber of deputies by coalition (2013 elections).
Coalition
统计学重点整理CH6-Continuous Probability Distributions
CH6Continuous distributionsContinuous distributions are constructed from continuous random variables in which values are taken for every point over a given intervalWith continuous distributions, probabilities of outcomes occurring between particular points are determined by calculating the area under the curve between these points Uniform DistributionThe uniform distribution is a relatively simple continuous distribution in which the same height f(x), is obtained over a range of valuesMean and standard deviation of a uniform distribution Mean μ = (a + b)/2Std Dev σ = (b -a)/Square root 12With discrete distributions, the probability function yields the value of the probabilityFor continuous distributions, probabilities are calculated by determining the area over an interval of the functionProperties of the Normal DistributionCharacteristics of the normal distribution:Continuous distribution - Line does not breakSymmetrical distribution - Each half is a mirror of the other halfAsymptotic to the horizontal axis - it does not touch the x axis and goes on forever Unimodal - means the values mound up in only one portion of the graph Area under the curve = 1 ; total of all probabilities = 1Probability Density Function of the Normal DistributionStandardized Normal DistributionZ score can be used to find probabilities for any normalcurve problem that has been converted to Z scores Z distribution is normal distribution with a mean of 0 and a Std Dev of 1 Exponential DistributionContinuousFamily of distributionsSkewed to the rightX varies from 0 to infinity 0 ,0for )(>≥=-λλλX X f eX⎪⎪⎩⎪⎪⎨⎧≤≤-=esother valu all 01)(for bx a fora b x f12DeviationStandard a b -=σ .. . 2.71828 . . . 3.14159 =X of deviation standard X of mean :21)(221===⎪⎭⎫ ⎝⎛-=-e Where x x f eπσμσμπσ σμ-=x zApex is always at X = 0Steadily decreases as X gets largerProbability functionP(x)=x 2−x 1b−aμ= 1λ()eX X X P 00λ-=≥ σ= 1λ。
统计学英文版
统计学英文版Part1GatheringandExploring Data (descriptive statistics)Different Types of Data (2.1) VariableA variable is any characteristic observed on the subjects in a study. Examples: Marital status, Height, Weight, IQ, Sqft, Price, NE.A variable can be classified as eitherCategorical (in Categories), orQuantitative (Numerical)A variable can be classified as categorical if each observation belongs to one of a set of categories:Examples:Gender (Male or Female)Religious Affiliation (Catholic, Jewish, …)Type of Residence (Apartment, Condo, …)Belief in Life After Death (Yes or No)NE (Located in northeast sector of city (1) or not (0) )A variable is called quantitative if observations on it take numerical values that represent different magnitudes of the variable. Examples:Age, Number of Siblings, Annual Income, Selling price, Sqft Discrete versus continuous quantitative variablesA quantitative variable is discrete if its possible values form a set ofseparate numbers, such as 0,1,2,3,…The set of possible values is not denseExamples:o Number of pets in a householdo Number of children in a familyo Number of foreign languages spoken by an individualA quantitative variable is continuous if its possible values form anintervalThe set of possible values is denseExamples:o Height/Weighto Ageo Blood pressureExerciseIdentify the variable type1.Number of siblings in a family2.County of residence3.Distance (in miles) of commute to school4.Marital status5.Length of time to take a test6.Number of people waiting in line7.Number of speeding tickets received last year8.Your dog’s weightProportion & Percentage (Relative Frequencies)The proportion of the observations that fall in a certain category is the frequency (count) of observations in that category divided by the total number of observations Frequency of that categorySum of all frequenciesThe percentage is the proportion multiplied by 100Proportions and percentages are also called relative frequenciesExampleTable classifies the 630 parliamentary seats of the Italian chamber of deputies by coalition (2013 elections).Coalition SeatsFreq. Prop. Perc.Pierluigi Bersani 345 0.548 54.8Silvio Berlusconi 125 0.198 19.8Beppe Grillo 109 0.173 17.3Mario Monti 47 0.075 7.46Vallee d'Aoste 1 0.002 0.16MAIAE 2 0.003 0.32USEI 1 0.002 0.16Antonio Ingroia 0 0 0Total 630 1 100so, for Grillo,345 is the frequency.0.548 = 345/630 is the proportion and relative frequency.54.8 is the percentage 0.548×100 = 54.8%.Frequency TableA frequency table is a listing of possible values for a variable, together with the number of observations and/or relative frequencies for each value.Raw data Frequency tableCode Gender Gender n i f i p i000001 F F 1000 0.01 1000002 M M 99000 0.99 99 ... ...100000 FExampleA stock broker has been following different stocks over the last month and has recorded whether a stock is up, the same, or down in value. The results were:1.Performance of stock Up Same DownCount 21 7 12What are the subjects?What is the variable of interest?What type of variable is it?Add proportions to this frequency table.Describe data using graphical summaries (2.2) DistributionA graph or frequency table describes a distribution.A distribution tells us the possible values/categories a variable takesas well as the occurrence of those values (frequency or relativefrequency or percentage)In the 2008 General Social Survey, 2020 respondents answered the question, "How many children have you ever had?" The results wereGraphs for categorical data: bar graphs and pie charts Use pie charts and bar graphs to summarize categorical variables: Pie Chart.o A circle where each category is represented as a “slice of the pie”o The size of each pie slice is proportional to the percentage ofobservations falling in that categoryBar Graph.o Bar Graphs display a vertical bar for each categoryo The height of each bar represents either counts (“frequencies”) or percentages (“relative frequencies”) for that categoryPie Chart52%18%17%13%Cars soldFIAT FORD OPEL RENAULTBar GraphBar graph: easier to compare categoriesBar graphs are called Pareto Charts when the categories are ordered by their frequency, from the tallest bar to the shortest barGraphs for quantitative data: dot plotShows a dot for each subject (observation) placed above its value on a number line. To construct a dot plotDraw a horizontal line and label it with the name of the variable. ?Mark regular values of the variable on it.For each observation, place a dot above its value on the number line.Graphs for quantitative data: histogramsA Histogram is a graph that uses bars to portray the frequencies or the relative frequencies of the possible outcomes for a quantitative variable Steps for constructing a histogram1.Divide the range of the data into intervals of equal width2.Count the number of observations in each interval, creating afrequency table3.On the horizontal axis, label the values or the endpoints of theintervals.4.Draw a bar over each value or interval with height equal to itsfrequency (or proportion or percentage), values of which are marked on the vertical axis.bel and title appropriatelyDisplaying Data over Time: time plotsUsed for displaying a time series, a data set collected over time.Plots each observation on the vertical scale against the time it was measured on the horizontal scale. Points are usually connected.Common patterns in the data over time, known as trends, should be noted.Measuring the Center of Quantitative Data (2.3)。
统计学要点摘要英文版-Statistic-Review
Chapter 2 Statistic ReviewA.Random variables；1.expected value:Define ：X is a discrete random variable, “ the mean (or expected value）of X " is the weighted average of the possible outcomes, where the probabilities of the outcomeserve as the appropriate weight.p i is ith of prob.，i=1，2, ……nInterpretation：The random variable is a variable that have a probability associated with each outcome. Outcome is not controlled。
Discrete random Var。
: has finite outcome，or outcome is countable infinite.Continuous random Var。
：uncountable infinite outcome，the probability of each outcome is small because of too many numbers.For normal random Var。
, probability density function is used to calculate the probability between the are.E( )：the expectations operator，→… “ sample mean”, used to estimateThe is changed from sample to sample. is not a fixed on time，the outcome selected should not be the same. There is prob。
统计学词汇中英文对照完整版
统计学词汇中英文对照完整版统计学词汇中英文对照完整版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 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Collinearity, 共线性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, 质量控制图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, 相关性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Crosstabs 列联表分析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 Estimation, 曲线拟合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Direct Oblimin, 斜交旋转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Error Bar, 均值相关区间图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Explained variance （已说明方差）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Generalized least squares, 综合最小平方法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High-Low, 低区域图Higher Order Interaction Effects，高阶交互作用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Image factoring,, 多元回归法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 Cluster逐步聚类分析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 Squared Criterion，最小二乘方准则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Leveage Correction，杠杆率校正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 distanceestimation, 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。
统计学专业英语词汇完整版
统计学专业英语词汇完整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/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 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Maximum L-estimator,极大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Unweighted least 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。
统计学英文
统计学英文Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In this article, we will discuss the key concepts and principles of statistics.Sample and PopulationStatistics is based on the idea of sampling. A sample is a subset of a population that is selected for analysis. The population is the entire group that is the subject of the study. For example, if we want to study the average age of university students in a country, the population is all the university students in the country. We cannot study the entire population, so we select a sample of students from different universities and use statistics to make inferences about the population based on the sample.Descriptive and Inferential StatisticsDescriptive statistics is concerned with summarizing and describing data. It includes measures of central tendency such as mean, median, and mode, and measures of variability such as range and standard deviation. Descriptive statistics helps us understand the characteristics of the data.Inferential statistics, on the other hand, is concerned with making conclusions about a population based on a sample. It involves testing hypotheses and estimating parameters. For example, we may want to test the hypothesis that the average age of university students in the country is 20 years. We would select a sample of students, calculate the sample mean, anduse statistical tests to determine whether the difference between the sample mean and the hypothesized population mean is significant.Variables and Data TypesA variable is a characteristic of a population or a sample that can take on different values. There are two types of variables: quantitative and qualitative. Quantitative variables are numerical, such as age, weight, and height. Qualitative variables are categorical, such as gender, ethnicity, and occupation.Data can be collected in different ways, such as through surveys, experiments, and observations. Data can also be classified into different types: nominal, ordinal, interval, and ratio. Nominal data are categorical, such as gender or race. Ordinal data are ranked, such as academic achievement or social status. Interval data are numerical, such as temperature or time, but lack a true zero point. Ratio data are numerical and have a true zero point, such as weight or height.Measures of Central TendencyMeasures of central tendency are used to summarize the data and provide a single value that represents the typical score. The three most commonly used measures of central tendency are the mean, median, and mode.The mean is the arithmetic average of the scores. It is calculated by adding up all the scores and dividing by the number of scores. The mean is sensitive to outliers, or extreme scores, which can skew the results.The median is the middle score when the scores are arranged in order. It is not affected by outliers and is a better measure of central tendency when the distribution is skewed.The mode is the most common score. It is useful for nominal data and can be used with ordinal data.Measures of VariabilityMeasures of variability are used to describe the spread or dispersion of the data. The most commonly used measures of variability are the range, variance, and standard deviation.The range is the difference between the largest and smallest scores. It is affected by outliers and is not a very reliable measure of variability.The variance is a measure of how much the scores deviate from the mean. It is calculated by subtracting each score from the mean, squaring the differences, and averaging the squares. The variance is not as intuitive as the other measures of variability, but it is useful for statistical analysis.The standard deviation is the square root of the variance. It is a more intuitive and commonly used measure of variability. The standard deviation is useful for determining how much the scores deviate from the mean and for estimating confidence intervals.Hypothesis TestingHypothesis testing is a process of determining whether a statement about a population is likely to be true or false based on a sample of data. The statement is called a null hypothesis, and the alternative to the null hypothesis is called the alternative hypothesis. We collect data and use statistics to test the null hypothesis.We use a significance level, or alpha, to determine whether the results are statistically significant. If the p-value is less than the significance level, we reject the null hypothesis and accept the alternative hypothesis.ConclusionStatistics is a powerful tool for analyzing and interpreting data. Understanding the concepts and principles of statistics is essential for making informed decisions and drawing accurate conclusions from data.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Relative frequency: assigning probabilities based on experimentation or historical data. Subjective approach: Assigning probabilities based on the assignor’s (subjective) judgment.
Copyright © 2009 Cengage Learning
6.3
Classical Approach…
Experiment: Rolling two dice and observing the total Outcomes: {2, 3, …, 12} Examples: P(2) = 1/36
Copyright © 2009 Cengage Learning
6.7
Interpreting Probability…
No matter which method is used to assign probabilities all will be interpreted in the relative frequency approach For example, a government lottery game where 6 numbers (of 49) are picked. The classical approach would predict the probability for any one number being picked as 1/49=2.04%. We interpret this to mean that in the long run each number will be picked 2.04% of the time.
Copyright © 2009 Cengage Learning
6.8
Joint, Marginal, Conditional Probability…
We study methods to determine probabilities of events that result from combining other events in various ways. There are several types of combinations and relationships between events: •Complement event •Intersection of events •Union of events •Mutually exclusive events •Dependent and independent events
A
Copyright © 2009 Cengage Learning
B
6.15
Mutually Exclusive vents…When two events are mutually exclusive (that is the two events cannot occur together), their joint probability is 0, hence:
Copyright © 2009 Cengage Learning
6.2
Classical Approach…
If an experiment has n possible outcomes, this method would assign a probability of 1/n to each outcome. It is necessary to determine the number of possible outcomes. Experiment: Outcomes Probabilities: Rolling a die {1, 2, 3, 4, 5, 6} Each sample point has a 1/6 chance of occurring.
The intersection is {(1,5)}
The joint probability of A and B is the probability of the intersection of A and B, i.e. P(A and B) = 1/36
Copyright © 2009 Cengage Learning
P(Total = 7) + P(Total not equal to 7) = 1
A
Copyright © 2009 Cengage Learning
Ac
6.11
Intersection of Two Events…
The intersection of events A and B is the set of all sample points that are in both A and B. The intersection is denoted: A and B The joint probability of A and B is the probability of the intersection of A and B, i.e. P(A and B)
“There is a 40% chance Bits & Bytes will sell 3 desktops on any given day”
Copyright © 2009 Cengage Learning
6.6
Subjective Approach…
“In the subjective approach we define probability as the degree of belief that we hold in the occurrence of an event” E.g. weather forecasting’s “P.O.P.” “Probability of Precipitation” (or P.O.P.) is defined in different ways by different forecasters, but basically it’s a subjective probability based on past observations combined with current weather conditions. POP 60% – based on current conditions, there is a 60% chance of rain (say).
6.5
Relative Frequency Approach…
Desktops Sold # of Days Desktops Sold
0 1 2 3 4
1 2 10 12 5
1/30 = .03 2/30 = .07 10/30 = .33 12/30 = .40 5/30 = .17 ∑ = 1.00
A
B
6.13
Union of Two Events…
The union of two events A and B, is the event containing all sample points that are in A or B or both: Union of A and B is denoted: A or B
A
B
6.12
Copyright © 2009 Cengage Learning
Intersection of Two Events…
For example, let A = tosses where first toss is 1 {(1,1), (1,2), (1,3), (1,4), (1,5), (1,6)} and B = tosses where the second toss is 5 {(1,5), (2,5), (3,5), (4,5), (5,5), (6,5)}
Bits & Bytes Computer Shop tracks the number of desktop computer systems it sells over a month (30 days): For example, 10 days out of 30 2 desktops were sold.
Desktops Sold # of Days
0
1
1
2
2
10 12 5
3 From this we can construct 4 the probabilities of an event (i.e. the # of desktop sold on a given day)…
Copyright © 2009 Cengage Learning
1 1 2 3 2 2 3 4 3 4 5 4 5 6 5 6 7 6 7 8
P(6) = 5/36 P(10) = 3/36
3
4
4
5
5
6
6
7
7
8
8
9
9
10
5
6
6
7
7
8
8
9
9
10
10
11
11
12
6.4
Copyright © 2009 Cengage Learning
Relative Frequency Approach…
Copyright © 2009 Cengage Learning
6.9
Complement of an Event…
The complement of event A is defined to be the event consisting of all sample points that are “not in A”. Complement of A is denoted by Ac The Venn diagram below illustrates the concept of a complement. P(A) + P(Ac )=1