statistic terms
统计学专业英语翻译
汉译英Population 总体，样本总体sample 样本，标本parameter 限制因素median 中位数odd 奇数，单数even 偶数range 极差variance 方差standard deviation 标准差Covariance 协方差empty event 空事件product event 积事件conditional probability 条件概率Random variable 随机变量binominal distribution 二项式分布uniform distribution 均匀分布Poisson distribution 泊松分布residual 残差central limit theorem 中心极限定律英译汉descriptive statistics 描述统计学mathematical statistics 数理统计学inductive statistics 归纳统计学Inferential statistics 推断统计学dimension 维，维数continuous variable 连续变量ordinal variable 有序变量nominal variable 名义变量dichotomous 两分的；二歧的discrete variable 离散变量categorical variable 分类变量location 定位，位置，场所dispersion 分散mean 均值unimodal单峰的multimodal 多峰的chaotic 无秩序的grouped data 分组数据frequency distribution频数分布cumulative frequency 累加频数tallying 计算Uniformly distribution 均匀分布histogram 直方图frequency polygon 频率多边图rectangle 矩形Percentile 百分位数quartile 四分位数interquartile range 四分位数间距simple event 简单事件Compound event 复合事件mutually exclusive 互斥的，互补相交的complementary event 对立事件Independent 独立的joint probability function 联合概率函数jacobian雅克比行列式Law of large numbers大数定律point estimate 点估计estimate 估计值statistic 统计量optimality 最优性Unbiased estimate 无偏估计量efficient estimate 有偏估计量unbiasedness无偏性efficience有效性Consistent estimate 一致估计量asymptotic properties 渐近性质Confidence interval 置信区间interval estimation 区间估计null hypothesis 原假设alternative hypothesis 备择假设significance level 显著性水平power function 幂函数testing procedures 检验方法test statistic 检验统计量rejection region 拒绝区域acceptance region 接受区域critical region 临界区域first-derivatives 一阶导数second-derivatives 二阶导数Likelihood ratio 似然比dependent variable因变量unexplanatory variable未解释变量independent variable自变量Error term 误差项regression coefficients 回归系数Sum of squared residuals 残差平方和Marginal probability function 边际概率函数joint probability density function 联合概率密度函数Marginal probability density function边际概率密度函数stochastically independent 随机独立的Mutually independently distribution 相互独立的分布independently and identically distribution 独立同分布的likelihood function 似然函数maximum likelihood estimator 最大似然估计量maximum likelihood estimate 最大似然估计值log-likelihood function 对数似然函数ordinary least squares estimation/estimate/estimator 普通最小二乘估计/估计值/估计量linear unbiased estimator 线性无偏估计第三章、概念与符号[An index]把指数定义成是对一组相关变量之中变化进行测算的一个实数。
Statistic Inference
• Sometimes we have some prior knowledge about the parameters • The prior information may not be accurate, so we can express it using a Prior distribution f ( ) • Using Bayesian theory(贝叶斯理论) to get the posterior distribution f ( | x) • The optimal parameter(最佳指数) can be estimated as
Conditional Independence
Conditional Independence
Conditional independence: dseparation
Conditional independence: dseparation
Conditional independence: dseparation
Introduction to graphical models
• A graph consists of nodes (vertices) that are connected by edges (links, arcs) • The graph can be directed (edges have arrows to indicate the direction) or undirected (edges do not have arrows) • In probabilistic graphical models each node in a graph represents a random variable and the edges of the graph represent probabilistic relationships between these variables • The joint distribution defined by a graph is given by the product, over all of the nodes of the graph, of a conditional distribution for each node conditioned on the variables corresponding to the parents of that node in the graph. K
statistic用法
statistic用法Statistic是一个英语单词，它的意思是“统计学”，也可以指“统计数据”。
在日常生活中，我们经常会用到Statistic这个词，尤其是在工作或学习中需要进行数据分析和处理的时候。
下面将详细介绍Statistic的用法。
一、作为名词使用1.1 指统计学Statistic作为名词时，最常见的用法是指“统计学”。
例如：- Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data.（统计学是数学的一个分支，它涉及数据的收集、分析、解释、展示和组织。
）- He has a degree in statistics.（他拥有一份统计学学位。
）1.2 指统计数据Statistic还可以指“统计数据”。
例如：- The statistic shows that the unemployment rate has increased by 2% in the last quarter.（这个统计数据显示，上个季度失业率增加了2%。
）- According to the statistics, the number of tourists visiting this city has doubled in the past year.（根据统计数据，去年来访该市的游客数量翻了一番。
）二、作为形容词使用Statistic还可以作为形容词使用，表示“统计的”、“统计学的”。
例如：- The statistic analysis of the data reveals some interesting patterns.（对数据进行的统计分析揭示了一些有趣的模式。
）- She is a statistician who specializes in statistical modeling.（她是一位专门从事统计建模的统计学家。
统计学专业英语词汇汇总
统计学复试专业词汇汇总population 总体sampling unit 抽样单元sample 样本observed value 观测值descriptive statistics 描述性统计量random sample 随机样本simple random sample 简单随机样本statistics 统计量order statistic 次序统计量sample range 样本极差mid-range 中程数estimator 估计量sample median 样本中位数sample moment of order k k阶样本矩sample mean 样本均值average 平均数arithmetic mean 算数平均值sample variance 样本方差sample standard deviation 样本标准差sample coefficient of variation 样本变异系数standardized sample random variable 标准化样本随机变量sample coefficient of skewness （歪斜）样本偏度系数sample coefficient of kurtosis (峰态) 样本峰度系数sample covariance 样本协方差sample correclation coefficient 样本相关系数standard error 标准误差interval estimator 区间估计statistical tolerance interval 统计容忍区间statistical tolerance limit 统计容忍限confidence interval 置信区间one-sided confidence interval 单侧置信区间prediction interval 预测区间estimate 估计值error of estimation 估计误差bias 偏倚unbiased estimator 无偏估计量maximum likelihood estimator 极大似然估计量estimation 估计maximum likelihood estimation 极大似然估计likelihood function 似然函数profile likelihood funtion 剖面函数hypothesis 假设null hypothesis 原假设alternative hypothesis 备择假设simple hypothesis 简单假设composite hypothesis 复合假设significance level 显著性水平type I error 第一类错误type II error 第二类错误statistical test 统计检验significance test 显著性检验p-value p值power of a test 检验功效power curve 功效曲线test statistic 检验统计量graphical descriptive statistics 图形描述性统计量numerical descriptive statistics 数值描述性统计量classes 类（组）class 类class 组class limits; class boundaries 组限mid-point of class 组中值class width 组距frequency 频数frequency distribution 频数分布histogram 直方图bar chart 条形图cumulative frequency 累积频数relative frequency 频率cumulative relative frequency 累积频率sample space 样本空间event 事件complementary event 对立事件independent events 独立事件probability [of an event A] [事件A的]概率conditional probability 条件概率distribution function [of a random variable X] [随机变量X的]分布函数family of distributions 分布族parameter 参数random variable 随机变量probability distribution 概率分布distribution 分布expectation 期望p-quantile; p-fractile p分位数median 中位数quartile 四分位数univariate probability distribution 一维概率分布univariate distribution 一维分布multivariate probability distribution 多维概率分布multivariate distribution 多维分布marginal probability distrubition 边缘概率分布marginal distribution 边缘分布conditional probability distribution 条件概率分布conditional distribution 条件分布regression curve 回归曲线regression surface 回归曲面discrete probability distribution 离散概率分布discrete distribution 离散分布continuous probability distribution 连续概率分布continuous distribution 连续分布probability [mass] function 概率函数mode of probability [mass] function 概率函数的众数probability density function 概率密度函数mode of probability density function 概率密度函数的众数discrete random variable 离散随机变量continuous random variable 连续随机变量centred probability distribution 中心化概率分布centred random variable 中心化随机变量standardized probability distribution 标准化概率分布standardized random variable 标准化随机变量moment of order r r阶[原点]矩means 均值moment of order r = 1 一阶矩mean 均值variance 方差standard deviation 标准差coefficient of variation 变异系数coefficient of skewness 偏度系数coefficient of kurtosis 峰度系数joint moment of order r and s (r,s)阶联合[原点]矩joint central moment of order r and s (r,s)阶联合中心矩covariance 协方差correlation coefficient 相关系数multinomial distribution 多项分布binomial distribution 二项分布Poisson distribution 泊松分布hypergeometric distibution 超几何分布negative binomial distribution 负二项分布normal distribution, Gaussian distribution 正态分布standard normal distribution, standard Gaussian distribution 标准正态分布lognormal distribution 对数正态分布t distribution; Student's distribution t分布degrees of freedom 自由度F distribution F分布gamma distribution 伽玛分布, Γ分布chi-squared distribution 卡方分布,χ2分布exponential distribution 指数分布beta distribution 贝塔分布,β分布uniform distribution, rectangular distribution 均匀分布type I value distribution; Gumbel distribution I型极值分布type II value distribution; Gumbel distribution II型极值分布Weibull distribution 威布尔分布type III value distribution; Gumbel distribution III型极值分布multivariate normal distribution 多维正态分布bivariate normal distribution 二维正态分布standard bivariate normal distribution 标准二维正态分布sampling distribution 抽样分布probability space 概率空间。
《交易心理分析》中英对照版
TRADING IN THE ZONE《交易心理分析》Trading in the zone : master the market with confidence, discipline, and a winning attitude / by Mark Douglas,《交易心理分析》用自信、纪律和赢家的态度来掌握市场/作者：马克·道格拉斯DEDICATION献词This book is dedicated to all of the traders I have had the pleasure of working with over the last 18 years as a trading coach. Each of you in your own unique way is a part of the insight and guidance this book will provide to those who choose to trade from a confident, disciplined, and consistent state of mind.本书献给过去18年来我所认识的所有交易者，作为交易教练，我和他们在一起很快乐。
你们中的每一个人用自己独特的方式，为本书提供见解和指导，引导那些用自信、纪律和持续一致思想交易的人们。
TABLE OF CONTENTS目录FOREWORD (4)序言 (4)PREFACE (5)引子 (5)ATTITUDE SURVEY (8)态度调查表 (8)CHAPTER 1 THE ROAD TO SUCCESS: FUNDAMENTAL, TECHNICAL, OR MENTAL ANALYSIS? 13第01 章成功之路：基本面，技术面或思想分析面？ (13)IN THE BEGINNING: FUNDAMENTAL ANALYSIS (13)开始：基本面分析 (13)THE SHIFT TO TECHNICAL ANALYSIS (14)转到技术分析 (14)THE SHIFT TO MENTAL ANALYSIS (15)转到思想分析 (15)CHAPTER 2 THE LURE (AND THE DANGERS) OF TRADING (25)第02章交易的诱惑（和危险） (25)THE ATTRACTION (26)吸引 (26)THE DANGERS (29)危险 (29)THE SAFEGUARDS (33)安全措施 (33)PROBLEM: The unwillingness to Create Rules (35)问题：不愿意制定原则 (35)PROBLEM: Failure to Take Responsibility (36)问题：不负责任 (36)PROBLEM: Addiction to Random Rewards (38)问题：对随机的回报上瘾 (38)PROBLEM: External versus Internal Control (39)问题：外部控制对抗内部控制 (39)CHAPTER 3 TAKING RESPONSIBILITY (40)第03章自己承担责任 (40)SHAPING YOUR MENTAL ENVIRONMENT (40)塑造你的思想环境 (40)REACTING TO LOSS (44)对亏损的反应 (44)WINNERS, LOSERS, BOOMERS, AND BUSTERS (54)赢家，输家，暴发户和亏损者 (54)CHAPTER 4 CONSISTENCY: A STATE OF MIND (60)第04章持续一致性：一种思想状态 (60)THINKING ABOUT TRADING (61)考虑交易 (61)REALLY UNDERSTANDING RISK (63)完全明白风险 (63)ALIGNING YOUR MENTAL ENVIRONMENT (67)让你的思想持续一致 (67)CHAPTER 5 THE DYNAMICS OF PERCEPTION (70)第05章认知的动力 (70)DEBUGGING YOUR MENTAL SOFTWARE (71)为你的思想软件找茬 (71)PERCEPTION AND LEARNING (74)认知和学习 (74)PERCEPTION AND RISK (79)认知和风险 (79)THE POWER OF ASSOCIATION (79)联想的力量 (79)CHAPTER 6 THE MARKET’S PERSPECTIVE (85)第06章市场的角度 (85)THE “UNCERTAINTY” PRINCIPLE (85)“不确定”原则 (85)MARKETS MOST FUNDAMENTAL CHARACTERISTIC (IT CAN EXPRESS ITSELF IN AN ALMOST INFINITE COMBINATION OF WAYS ) (90)市场最基本的特征（它几乎可以用任何组合方式来表达自己） (90)CHAPTER 7 THE TRADER’S EDGE: THINKING IN PROBABILITIES (96)第07章交易者的优势：考虑概率 (96)PROBABILITIES PARADOX: RANDOM OUTCOME, CONSISTENT RESULTS (96)概率的似非而是：随机的结果，持续一致的收入 (96)TRADING IN THE MOMENT (100)有机会就交易 (100)MANAGING EXPECTATIONS (106)管理期望 (106)ELIMINATING THE EMOTIONAL RISK (112)消灭情绪风险 (112)CHAPTER 8 WORKING WITH YOUR BELIEFS (115)第08章和信念一起工作 (115)DEFINING THE PROBLEM (116)给问题下定义 (116)DEFINING THE TERMS (118)对几个说法下定义 (118)HOW THE FUNDAMENTAL TRUTHS RELATE TO THE SKILLS (120)这些基本事实和技术如何联系起来 (120)MOVING TOWARD “THE ZONE” (123)进入这个“状态” (123)CHAPTER 9 THE NATURE OF BELIEFS (124)第09章信念的天性 (124)THE ORIGINS OF A BELIEF (126)信念的来源 (126)BELIEFS AND THEIR IMPACT ON OUR LIVES (128)信念和信念对我们生活的影响 (128)BELIEFS VS. THE TRUTH (133)信念对抗事实 (133)CHAPTER 10 THE IMPACT OF BELIEFS ON TRADING (135)第10章信念对交易的影响 (135)THE PRIMARY CHARACTERISTICS OF A BELIEF (136)信念的基本特点 (136)SELF EVALUATION AND TRADING (148)自我评估和交易 (148)CHAPTER 11 THINKING LIKE A TRADER (150)第11章像交易者一样思考 (150)THE MECHANICAL STAGE (152)机械阶段 (152)THE ROLE OF SELF-DISCIPLINE (157)自律的作用 (157)CREATING A BELIEF IN CONSISTENCY (162)创造一个相信持续一致性的信念 (162)EXERCISE: LEARNING TO TRADE AN EDGE LIKE A CASINO (165)练习：向赌场学习利用优势交易 (165)A FINAL NOTE (176)最后的提醒 (176)ATTITUDE SURVEY (176)态度调查 (176)INDEX ........................................................................................... 错误！未定义书签。
统计学必考
统计学（Statistics）：运用概率论、数理统计的原理与方法，研究数据的搜集；分析；解释；表达的科学。
总体（population）：大同小异的研究对象全体。
更确切的说，总体是指根据研究目的确定的、同质的全部研究单位的观测值。
样本（sample）：来自总体的部分个体，更确切的说，应该是部分个体的观察值。
样本应该具有代表性，能反映总体的特征。
利用样本信息可以对总体特征进行推断。
抽样误差（sampling error）在抽样过程中由于抽样的偶然性而出现的误差。
表现为总体参数与样本统计量的差异，以及多个样本统计量之间的差异。
可用标准误描述其大小。
标准误(Standard Error) 样本统计量的标准差，反映样本统计量的离散程度，也间接反映了抽样误差的大小。
参数估计：指用样本指标值（统计量）估计总体指标值（参数）。
参数估计有两种方法：点估计和区间估计。
区间估计（interval estimation）：将样本统计量与标准误结合起来，确定一个具有较大臵信度的包含总体参数的范围，该范围称为臵信区间（confidence interval，CI），又称可信区间。
频数表（frequency table）用来表示一批数据各观察值或在不同取值区间的出现的频繁程度参考值范围描述绝大多数正常人的某项指标所在范围；正态分布法（标准差）、百分位数法，参考值范围用于判断某项指标是否正常置信区间：在统计学中，一个概率样本的置信区间（Confidence interval）是对这个样本的某个总体参数的区间估计。
置信区间展现的是这个参数的真实值有一定概率落在测量结果的周围的程度。
给出的是被测量参数的测量值的可信程度。
完全随机设计（completely random design）：完全随机设计仅涉及一个处理因素（但可为多水平），故又称单因素（one-way）设计。
它是将受试对象按随机化的方法分配到各个处理组中，观察实验效应，临床试验中的随机对照试验也属于此类设计。
i2_-statistic_统计方法_概述及解释说明
i2 -statistic 统计方法概述及解释说明1. 引言1.1 概述i2-statistic统计方法作为一种重要的统计学工具，被广泛应用于各个领域的数据分析与研究中。
它是一种非参数性质的统计指标，能够在无需依赖特定分布假设的情况下，对数据进行有效的推断和分析。
本篇文章将全面介绍i2-statistic 方法的基本概念、原理、主要特点以及在实际应用中所取得的成果。
1.2 文章结构本文共分为五个部分进行阐述。
除了引言部分外，还包括i2-statistic统计方法的基本概念和原理、主要特点和优势以及应用案例和实际意义等内容。
最后，在结论部分对i2-statistic方法进行总结，并展望其未来研究方向及潜力问题。
1.3 目的通过这篇文章，旨在让读者了解和掌握i2-statistic统计方法在各个领域中的应用价值以及其在数据分析与研究中所起到的重要作用。
同时，也希望能够引起读者对未来发展方向和潜力问题的思考。
通过阅读本文，读者将能够全面了解i2-statistic方法，并为自己的研究工作提供有益的参考和指导。
2. i2-statistic统计方法的基本概念和原理：2.1 i2-statistic概述：i2-statistic是一种用于分析数据之间异质性的统计方法。
它是由Kohnen等人提出的，用来衡量不同因素（如个体、群体、地区等）在某一特定变量上的差异程度。
i2-statistic是对方差分析方法的一个扩展应用，适用于多组数据比较。
2.2 i2-statistic的统计学原理：i2-statistic基于方差分析的思想，通过比较观察值与期望值之间的差异来判断数据之间是否存在显著差异。
具体而言，i2-statistic通过计算两个因素间变异性总和与系统误差总和（或残留误差总和）之比得到。
这个比值越大，则表示两个因素之间的差异越显著。
在应用i2-statistic时需要进行一些假设检验，比如独立性假设、均值相等假设等。
统计学概论主要术语
第1章统计学研究什么？主要术语1. 统计学(statistics)：收集、处理、分析、解释数据并从数据中得出结论的科学。
2. 描述统计（descriptive statistics）：研究数据收集、处理和描述的统计学方法。
3. 推断统计（inferential statistics）：研究如何利用样本数据来推断总体特征的统计学方法。
4. 变量(variable)：每次观察都会得到不同结果的某种特征。
5. 分类变量(categorical variable)：又称无序分类变量，观测结果表现为某种类别的变量。
6. 顺序变量(rank variable)：又称有序分类变量，观测结果表现为某种有序类别的变量。
7. 数值变量(metric variable)：又称定量变量，观测结果表现为数字的变量。
8. 分类数据(categorical data)：只能归于某一类别的非数字型数据。
9. 顺序数据(rank data)：只能归于某一有序类别的非数字型数据。
10. 数值型数据(metric data)：按数字尺度测量的数据。
11. 总体（population）：包含所研究的全部个体（数据）的集合。
12. 样本（sample）：从总体中抽取的一部分元素的集合。
13. 样本量（sample size）：构成样本的元素的数目。
14. 简单随机抽样（simple random sampling）：从含有N个元素的总体中，抽取n个元素组成一个样本，使得总体中的每一个元素都有相同的机会（概率）被抽中。
15. 分层抽样（stratified sampling）：也称分类抽样，在抽样之前先将总体的元素划分为若干层（类），然后从各个层中抽取一定数量的元素组成一个样本。
16. 系统抽样（systematic sampling）：也称等距抽样，先将总体各元素按某种顺序排列，并按某种规则确定一个随机起点，然后每隔一定的间隔抽取一个元素，直至抽取n个元素组成一个样本。
统计学术语中英对照
统计学术语中英对照部门： xxx时间： xxx整理范文，仅供参考，可下载自行编辑population 母体sample 样本census 普查sampling 抽样quantitative 量的qualitative/categorical 质的discrete 离散的continuous 连续的population parameters 母体参数sample statistics 样本统计量descriptive statistics 叙述统计学inferential/inductive statistics 推论 ...抽样调查<sampliing survey单纯随机抽样<simple random sampling系统抽样<systematic sampling分层抽样<stratified sampling整群抽样<cluster sampling多级抽样<multistage sampling常态分配(Parametric Statistics>无母数统计学(Nonparametric Statistics>实验设计(Design of Experiment>参数(Parameter>Data analysis 资料分析Statistical table 统计表Statistical chart 统计图Pie chart 圆饼图Stem-and-leaf display 茎叶图Box plot 盒须图Histogram 直方图Bar Chart 长条图Polygon 次数多边图Ogive 肩形图Descriptive statistics 叙述统计学Expectation 期望值Mode 众数Mean 平均数Variance 变异数Standard deviation 标准差Standard error 标准误Covariance matrix 共变异数矩阵Inferential statistics 推论统计学Point estimation 点估计Interval estimation 区间估计Confidence interval 信赖区间Confidence coefficient 信赖系数Testing statistical hypothesis 统计假设检定Regression analysis 回归分析Analysis of variance 变异数分析Correlation coefficient 相关系数Sampling survey 抽样调查Census 普查Sampling 抽样Reliability 信度Validity 效度Sampling error 抽样误差Non-sampling error 非抽样误差Random sampling 随机抽样Simple random sampling 简单随机抽样法Stratified sampling 分层抽样法Cluster sampling 群集抽样法Systematic sampling 系统抽样法Two-stage random sampling 两段随机抽样法Convenience sampling 便利抽样Quota sampling 配额抽样Snowball sampling 雪球抽样Nonparametric statistics 无母数统计The sign test 等级检定Wilcoxon signed rank tests 魏克森讯号等级检定Wilcoxon rank sum tests 魏克森等级和检定Run test 连检定法Discrete uniform densities 离散的均匀密度Binomial densities 二项密度Hypergeometric densities 超几何密度Poisson densities 卜松密度Geometric densities 几何密度Negative binomial densities 负二项密度Continuous uniform densities 连续均匀密度Normal densities 常态密度Exponential densities 指数密度Gamma densities 伽玛密度Beta densities 贝他密度Multivariate analysis 多变量分析Principal components 主因子分析Discrimination analysis 区别分析Cluster analysis 群集分析Factor analysis 因素分析Survival analysis 存活分析Time series analysis 时间序列分析Linear models 线性模式Quality engineering 品质工程Probability theory 机率论Statistical computing 统计计算Statistical inference 统计推论Stochastic processes 随机过程Decision theory 决策理论Discrete analysis 离散分析Mathematical statistics 数理统计统计学 : Statistics母体 : Population样本 : Sample资料分析 : Data analysis统计表 : Statistical table统计图 : Statistical chart圆饼图 : Pie chart茎叶图 : Stem-and-leaf display盒须图 : Box plot直方图 : Histogram长条图 : Bar Chart次数多边图 : Polygon肩形图 : Ogive叙述统计学 : Descriptive statistics期望值 : Expectation众数 : Mode平均数 : Mean变异数 : Variance标准差 : Standard deviation标准误 : Standard error共变异数矩阵 : Covariance matrix推论统计学 : Inferential statistics点估计 : Point estimation区间估计 : Interval estimation信赖区间 : Confidence interval信赖系数 : Confidence coefficient统计假设检定 : Testing statistical hypothesis回归分析 : Regression analysis变异数分析 : Analysis of variance相关系数 : Correlation coefficient抽样调查 : Sampling survey普查 : Census抽样 : Sampling信度 : Reliability效度 : Validity抽样误差 : Sampling error非抽样误差 : Non-sampling error随机抽样 : Random sampling简单随机抽样法 : Simple random sampling分层抽样法 : Stratified sampling群集抽样法 : Cluster sampling系统抽样法 : Systematic sampling两段随机抽样法 : Two-stage random sampling便利抽样 : Convenience sampling配额抽样 : Quota sampling雪球抽样 : Snowball sampling无母数统计 : Nonparametric statistics等级检定 : The sign test魏克森讯号等级检定 : Wilcoxon signed rank tests魏克森等级和检定 : Wilcoxon rank sum tests连检定法 : Run test离散的均匀密度 : Discrete uniform densities二项密度 : Binomial densities超几何密度 : Hypergeometric densities卜松密度 : Poisson densities几何密度 : Geometric densities负二项密度 : Negative binomial densities连续均匀密度 : Continuous uniform densities常态密度 : Normal densities指数密度 : Exponential densities伽玛密度 : Gamma densities贝他密度 : Beta densities多变量分析 : Multivariate analysis主因子分析 : Principal components区别分析 : Discrimination analysis群集分析 : Cluster analysis因素分析 : Factor analysis存活分析 : Survival analysis时间序列分析 : Time series analysis线性模式 : Linear models品质工程 : Quality engineering机率论 : Probability theory统计计算 : Statistical computing统计推论 : Statistical inference随机过程 : Stochastic processes决策理论 : Decision theory离散分析 : Discrete analysis数理统计 : Mathematical statistics统计名词市调辞典众数(Mode> 普查(census>指数(Index> 问卷(Questionnaire>中位数(Median> 信度(Reliability>百分比(Percentage> 母群体(Population> 信赖水准(Confidence level> 观察法(Observational Survey> 假设检定(Hypothesis Testing> 综合法(Integrated Survey> 卡方检定(Chi-square Test> 雪球抽样(Snowball Sampling> 差距量表(Interval Scale> 序列偏差(Series Bias> 类别量表(Nominal Scale> 次级资料(Secondary Data> 顺序量表(Ordinal Scale> 抽样架构(Sampling frame> 比率量表(Ratio Scale> 集群抽样(Cluster Sampling>连检定法(Run Test> 便利抽样(Convenience Sampling> 符号检定(Sign Test> 抽样调查(Sampling Sur>算术平均数(Arithmetic Mean> 非抽样误差(non-sampling error> 展示会法(Display Survey> 调查名词准确效度(Criterion-Related Validity> 元素(Element> 邮寄问卷法(Mail Interview> 样本(Sample> 信抽样误差(Sampling error> 效度(Validity> 封闭式问题(Close Question>精确度(Precision> 电话访问法(Telephone Interview> 准确度(Validity> 随机抽样法(Random Sampling> 实验法(Experiment Survey>抽样单位(Sampling unit> 资讯名词市场调查(Marketing Research> 决策树(Decision Trees>容忍误差(Tolerated erro> 资料采矿(Data Mining>初级资料(Primary Data> 时间序列(Time-Series Forecasting>目标母体(Target Population> 回归分析(Regression>抽样偏差(Sampling Bias> 趋势分析(Trend Analysis>抽样误差(sampling error> 罗吉斯回归(Logistic Regression>架构效度(Construct Validity> 类神经网络(Neural Network>配额抽样(Quota Sampling> 无母数统计检定方法(Non-Parametric Test>人员访问法(Interview> 判别分析法(Discriminant Analysis>集群分析法(cluster analysis> 规则归纳法(Rules Induction>内容效度(Content Validity> 判断抽样(Judgment Sampling>开放式问题(Open Question> OLAP(Online Analytical Process>分层随机抽样(Stratified Random sampling> 资料仓储(Data Warehouse>非随机抽样法(Nonrandom Sampling> 知识发现(Knowledge DiscoveryAbsolute 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Categoricalvariable, 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Centraltendency, 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b5E2RGbCAP 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p1EanqFDPw 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, 双盲实验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, 实验抽样Experimental unit, 实验单位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, 析因实验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finitepopulation, 有限总体Finite-sample, 有限样本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Infinitepopulation, 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M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DXDiTa9E3dKurtosis, 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。
统计学相关术语
统计学相关术语1、概率（proability）：度量一随机事件发生可能性大小的实数，其值介于0 与1 之间。
一随机事件的慨率可看作在相同条件下重复试验时，该事件发生的频率的稳定值，也可看作对事件发生的相信程度。
2、统计学（statistics）：主要通过利用概率论建立数学模型，收集所观察系统的数据，进行量化的分析、总结，并进而进行推断和预测，为相关决策提供依据和参考。
也就是收集、处理、分析、解释数据并从数据中得出结论的科学。
主要又分为描述统计学和推断统计学。
3、描述统计（Descriptive statistics）：描述统计是通过图表或数学方法，对数据资料进行整理、分析，并对数据的分布状态、数字特征和随机变量之间关系进行估计和描述的方法。
目的是描述数据特征，找出数据的基本规律。
描述统计分为集中趋势分析和离中趋势分析和相关分析三大部分。
4、推断统计（Inferential Statistics）：推断统计是研究如何根据样本数据来推断总体数量特征的方法，它是在对样本数据进行描述的基础上，对统计总体的未知数量特征做出以概率形式表述的推断。
主要包括参数估计与假设检验两种方法。
描述统计学和推断统计学的划分，一方面反映了统计方法发展的前后两个阶段，同时也反映了应用统计方法探索客观事物数量规律性的不同过程。
5、数值型数据(metric data)：按数字尺度测量的观察值，结果表现为具体的数值，对事物的精确测度，例如：身高为175cm、168cm、183cm。
6、分类数据(categorical data) ：只能归于某一类别的非数字型数据，对事物进行分类的结果，数据表现为类别，用文字来表述，例如，人口按性别分为男、女两类。
7、总体(population)：所研究的全部个体(数据) 的集合，其中的每一个个体也称为元素。
分为有限总体和无限总体：有限总体的范围能够明确确定，且元素的数目是有限的；无限总体所包括的元素是无限的，不可数的。
《Business-statistic》复习参考(第一章至第八章)
《Business Statistic》中国人民大学出版社英文版第五版chapter1~8复习参考Part1名词解释1、Statistics is a method of extracting useful information from a set of numerical data in order tomake a more effective and informed decision.2、Descriptive Statistics：These are statistical methods of organizing, summarizing andpresenting numerical data in convenient forms such as graphs, charts and tables.3、Inferential statistics is defined as statistical methods used for drawing conclusions about apopulation based on samples.4、Primary data is obtained first hand.5、Secondary data already exists or has been previously collected such as company accounts, orsales figures.6、Mean: The arithmetic average and the most common measure ofaaaaaaa central tendency. ①All values are included in computing the mean.②A set of data has a unique mean ③The mean is affected by unusually large or small data points (outliers / extreme values).7、Mode: The most frequent data, or data corresponding to the highest frequency. ①Mode is notaffected by extreme values. ②There may not be a mode. ③There may be several modes. ④Used for either numerical or categorical data.8、Median is the value that splits a ranked set of data into two equal parts. ①Median is notaffected by extremely large or small values and is therefore a valuable measure of central tendency when such values occur.9、Standard Deviation: ①A measure of the variation of data from the mean. ②The mostcommonly used measure of variation. ③Represented by the symbol ‘s’. ④Shows how the data is distributed around the mean.10、Probability is the chance of an occurrence of an event. ①Probability of an eventalways lies between 0 and 1. ②The sum of the probabilities of every possible outcome or event is 1. ③The probability of the complement A’ is given by 1-P(A).11、Properties of Normal distribution：①Continuous random variable. ②‘Bell-shaped’ &symmetrical. ③Mean, median, mode are equal ④Area under the curve is 1.12、The Central Limited Theorem：①If the population followed normal distribution, thesampling distribution of mean is followed normal distribution. ②If the population do not followed normal distribution, but the sample size is larger than 30, the sampling distribution of mean is followed normal distribution.Part2选择题Topic 1 - Introduction to Business Statistics & Data CollectionQ1. The universe or totality of items or things under consideration is called:a. a sample.b. a population.c. a parameter.d.none of the above.Q2. Those methods involving the collection, presentation, and characterization of a set of data in order to properly describe the various features of that set of data are called:a.inferential statistics.b.total quality management.c.sampling.d.descriptive statistics.Q3. The portion of the universe that has been selected for analysis is called:a. a sample.b. a frame.c. a parameter.d. a statistic.Q4. A summary measure that is computed to describe a numerical characteristic from only a sample of the population is called:a. a parameter.b. a census.c. a statistic.d.the scientific method.Q5. A summary measure that is computed to describe a characteristic of an entire population is called:a. a parameter.b. a census.c. a statistic.d.total quality management.Q6. The process of using sample statistics to draw conclusions about population parameters is called:a.inferential statistics.b.experimentation.c.primary sources.d.descriptive statistics.Q7. Which of the four methods of data collection is involved when a person retrieves data from an online database?a.published sources.b.experimentation.c.surveying.d.observation.Q8. Which of the four methods of data collection is involved when people are asked to complete a questionnaire?a.published sources.b.experimentation.c.surveying.d.observation.Q9. Which of the four methods of data collection is involved when a person records the use of the Los Angeles freeway system?a.published sources.b.experimentation.c.surveying.d.observation.Q10. A focus group is an example of which of the four methods of data collection?a.published sources.b.experimentation.c.surveying.d.observation.Q11. Which of the following is true about response rates?a.The longer the questionnaire, the lower the rate.b.Mail surveys usually produce lower response rates than personal interviews or telephonesurveys.c.Question wording can affect a response rate.d. d. All of the above.Q12. Which of the following is a reason that a manager needs to know about statistics?a.To know how to properly present and describe information.b.To know how to draw conclusions about the population based on sample information.c.To know how to improve processes.d.All of the above.Scenario 1-1Questions 13-15 refer to this scenario:An insurance company evaluates many variables about a person before deciding on an appropriate rate for automobile insurance. Some of these variables can be classified as categorical, discrete and numerical, or continuous and numerical.Q13. Referring to Scenario 1-1 (above), the number of claims a person has made in the last three years is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q14. Referring to Scenario 1-1 (above), a person's age is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q15. Referring to Scenario 1-1 (above), a person's gender is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q16. Which of the following can be reduced by proper interviewer training?a.Sampling error.b.Measurement error.c.Coverage error.d.Nonresponse error.Scenario 1-2Questions 17-19 refer to this scenario:Mediterranean fruit flies were discovered in California a few years ago and badly damaged the oranges grown in that state. Suppose the manager of a large farm wanted to study the impact of the fruit flies on the orange crops on a daily basis over a 6-week period. On each day a random sample of orange trees was selected from within a random sample of acres. The daily average number of damaged oranges per tree and the proportion of trees having damaged oranges were calculated.Q17. Referring to Scenario 1-2 (above), the two main measures calculated each day (i.e., average number of damaged oranges per tree and proportion of trees having damaged oranges) are called _______.a.statistics.b.parameters.c.samples.d.populations.Q18. Referring to Scenario 1-2 (above), the two main measures calculated each day (i.e., average number of damaged oranges per tree and proportion of trees having damaged oranges) may be used on a daily basis to estimate the respective true population _______.a.estimates.b.parameters.c.statistics.d.frame.Q19. Referring to Scenario 1-2 (above), in this study, drawing conclusions on any one day about the true population characteristics based on information obtained from the sample is called _______.a.evaluation.b.descriptive statistics.c.inferential statistics.d.survey.Scenario 1-3Questions 20 and 21 refer to this scenario:The Quality Assurance Department of a large urban hospital is attempting to monitor and evaluate patient satisfaction with hospital services. Prior to discharge, a random sample of patients is asked to fill out a questionnaire to rate such services as medical care, nursing, therapy, laboratory, food, and cleaning. The Quality Assurance Department prepares weekly reports that are presented at the Board of Directors meetings and extraordinary/atypical ratings are easy to flag.Q20. Referring to Scenario 1-3 (above), true population characteristics estimated from the sample results each week are called _____________.a.inferences.b.parameters.c.estimates.d.data.Q21. Referring to Scenario 1-3 (above), a listing of all hospitalised patients in this institution over a particular week would constitute the ________.a.sample.b.population.c.statistics.d.parameters.Scenario 1-4Questions 22-24 refer to this scenario:The following are the questions given to Sheila Drucker-Ferris in her college alumni association survey. Each variable can be classified as categorical or numerical, discrete or continuous.Q22. Referring to Scenario 1-4 (above), the data for the number of years since graduation is categorised as: __________________.a.numerical discrete.b.categorical.c.numerical continuous.d.none of the above.Q23. Referring to Scenario 1-4 (above), the data for the number of science majors is categorised as: ____________.a.categorical.b.numerical continuous.c.numerical discrete.d.none of the above.Q24. Referring to Scenario 1-4 (above), the data for tabulating the level of job satisfaction (High, Moderate, Low) is categorised as: _________.a.numerical continuous.b.categorical.c.numerical discrete.d.none of the above.Topic 2: Organising and Presenting dataQ1 The width of each bar in a histogram corresponds to the:a.boundaries of the classes.b.number of observations in the classes.c.midpoint of the classes.d.percentage of observations in the classes.Q2 When constructing charts, which of the following chart types is plotted at the class midpoints?a.Frequency histograms.b.Percentage polygons.c.Cumulative relative frequency ogives.d.Relative frequency histograms.Q3 When polygons or histograms are constructed, which axis must show the true zero or "origin"?a.The horizontal axis.b.The vertical axis.c.Both the horizontal and vertical axes.d.Neither the horizontal nor the vertical axis.Q4 To determine the appropriate width of each class interval in a grouped frequency distribution, we:a.divide the range of the data by the number of desired class intervals.b.divide the number of desired class intervals by the range of the datac.take the square root of the number of observations.d.take the square of the number of observations.Q5 When grouping data into classes it is recommended that we have:a.less than 5 classes.b.between 5 and 15 classes.c.more than 15 classes.d.between 10 and 30 classes.Q6 Which of the following charts would give you information regarding the number of observations "up to and including" a given group?a.Frequency histograms.b.Polygons.c.Percentage polygons.d.Cumulative relative frequency ogives.Q7 Another name for an "ogive" is a:a.frequency histogram.b.polygon.c.percentage polygon.d.cumulative percentage polygon.Q8 In analyzing categorical data, the following graphical device is NOT appropriate:a.bar chart.b.Pareto diagram.c.stem and leaf display.d.pie chart.Table 2The opinions of a sample of 200 people broken down by gender about the latest congressionalthe latest congressional plan to eliminate anti-trust exemptions for professional baseball. Referring to Table 2, the number of people who are neutral to the plan is _______.a.36b.54c.90d.200Q10 Referring to Table 2, the number of males who are against the plan is _______.a.12b.48c.60d.96Q11 Referring to Table 2, the percentage of males among those who are for the plan is ______.a.12.5%b.24%c.25%d.76%Q12 Referring to Table 2, the percentage who are against the plan among the females is _______.a.11.54%b.20%c.30%d.52%Topic 3: Numerical Descriptive StatisticsQ1 Which measure of central tendency can be used for both numerical and categorical variables?a.Mean.b.Median.c.Mode.d.Quartiles.Q2 Which of the following statistics is not a measure of central tendency?a.Mean.b.Median.c.Mode.d.Q3.Q3 Which of the following statements about the median is NOT true?a.It is more affected by extreme values than the mean.b.It is a measure of central tendency.c.It is equal to Q2.d.It is equal to the mode in bell-shaped distributions.Q4 The value in a data set that appears most frequently is called:a.the median.b.the mode.c.the mean.d.the variance.Q5 In a perfectly symmetrical distribution:a.the mean equals the median.b.the median equals the mode.c.the mean equals the mode.d.All of the above.Q6 When extreme values are present in a set of data, which of the following descriptive summary measures are most appropriate?a.CV and range.b.Mean and standard deviation.c.Median and interquartile range.d.Mode and variance.Q7 The smaller the spread of scores around the mean:a.the smaller the interquartile range.b.the smaller the standard deviation.c.the smaller the coefficient of variation.d.All the above.Q8 In a right-skewed distribution:a.the median equals the mean.b.the mean is less than the median.c.the mean is greater than the median.d.the mean is less than the mode.a.15.25b.19.73c.21.42d.21.70Q10 Referring to Table 3 (above), the median carbohydrate amount in the cereal is ________ grams.a.19b.20c.21d.21.5Q11 Referring to Table 3 (above), the 1st quartile of the carbohydrate amounts is ________ grams.a.15b.20c.21d.25Q12 Referring to Table 3 (above), the range in the carbohydrate amounts is ________ grams.a.16b.18c.20d.21Topic 4: Basics probability and discrete probability distributionsInformation A, needed to answer Questions 1 to 2The Health and Safety committee in a large retail firm is examining the relationship between the number of days of sick leave an employee takes and whether an employee works on the day shift (D) or night shift (N). The committee looks at a sample of 50 employees and notes which shift they work on and whether the number of days of sick leave they take in a year is less than 6 daysin the table of probabilities is not correct?a.The probability of an employee taking 6 or more days of sick leave P(M) is 0.6b.The probability that an employee is on the Night Shift (N) and takes less than 6 days ofleave (L), is called a conditional probability P(N | L) = 0.6c.If you know that an employee is on day shift (D) then the probability that they will takeless than 6 days of leave (L) is the conditional probability P(L | D) = 0.4d.The probability that an employee works Day Shift (D) or takes 6 or more days of leave(M) is found using the addition rule to be P(D or M) = 0.76e.They are all correctQ2 The analyst wishes to use the Probabilities table from Information A to determine whether the work shift variable and the number of days of sick leave variable are or are not independentvariables. Which of the following statements about the work shift and the number of days of sickleave variables is correct ?a.These variables are independent because the marginal probabilities such as P(L) are thesame as the conditional probabilities P(L | D)b.These variables are not independent because the marginal probability P(L) is differentfrom the conditional probability P(N | L)c.These variables are not independent because the joint probabilities such as P(L and N) areequal to the product of the probabilities P(L).P(N).d.These variables are dependent because the marginal probabilities such as P(L) are equalto the conditional probability P(L | N)e.None of the aboveInformation B, needed to answer Question 3Suppose the manager of a home ware retailer decides in a 5-minute period no more than 4 customers can arrive at a counter. Using past records he obtains the following probabilitythe following is the correct pair of values for the mean, the variance or standard deviation of the number of arrivals at the counter.a.Mean mu = 2 and variance sigma-squared = 1.265b.Mean mu = 2.5 and variance sigma-squared = 1.6c.Mean mu = 2 and standard deviation sigma = 1.6d.Mean mu = 2.4 and variance sigma-squared = 1.6e.None of the aboveInformation C, needed to answer Questions 4-6The section manager in an insurance company is interested in evaluating how well staff at the inquiry counter handle customer complaints. She interviews a sample of n = 6 customers who have made complaints and asks each of them whether staff had handled their complaints well. Each interview is called a trial. If a customer says their complaint was handled well this is called a success. She thinks that as long as these people are interviewed independently of each other then the number of people who say their complaint was handled well is a random variable with a Binomial probability distribution. The section manager thinks that the probability that a customers complaint will be handled well is p = 0.75.Q4 Use Information C to answer this question. A total of n = 6 people are interviewed independently of each other. Which of the following statements about the probability that 5 out of the 6 complaints will be handled well is correcta.less than 0.06b.between 0.23 and 0.24c.more than 0.35d.between 0.30 and 0.32e.None of the aboveQ5 Using Information C, which of the following statements about the probability that 4 or less of the 6 complaints will be handled well is correcta.less than 0.36b.more than 0.52c.between 0.45 and 0.475d.between 0.15 and 0.175e.None of the aboveQ6 Suppose the section manager from Information C is interested in the measures of central tendency and variation for the number of complaints which are handled well. Which of the following sets of values, where values are rounded to 3 decimal places, is the correct set of valuesa.Mean mu = 4.5 and variance sigma-squared = 1.125b.Mean mu = 4.5 and variance sigma-squared = 1.061c.Mean mu = 1.5 and variance sigma-squared = 1.125d.Mean mu = 1.5 and standard deviation sigma = 1.061e.None of the aboveInformation D, needed to answer Questions 7-9The manager of a large retailer thinks that one reason why staff at the complaints counter fail to handle customer complaints well is that not enough staff are allocated to this counter. Past experience has shown that the number of customers who arrive at this counter has a Poisson distribution where the average number who arrive each hour is 36. He decides to look at how many customers are likely to arrive at the complaints counter during a 5-minute period.Q7 Use Information D to answer this question. Which of the following statements concerning the probability that exactly 2 customers will arrive at the counter in a 5-minute period is correcta.less than 0.05b.between 0.21 and 0.23c.between 0.16 and 0.18d.more than 0.25e.None of the aboveQ8 Use Information D to answer this question. Which of the following statements concerning the probability that 3 or more customers will arrive at a counter in a 5-minute period is correcta.between 0.10 and 0.15b.less than 0.23c.more than 0.77d.between 0.60 and 0.55e.None of the aboveQ9 The section manager from Information D is interested in the mean and variance of the number of customers who arrive during a 1 hour period. Which of the following is the correct set of values for these two measuresa.Mean mu = 3 and variance sigma-squared = 3b.Mean mu = 36 and standard deviation sigma = 1.732c.Mean mu = 30 and variance sigma-squared = 30d.Mean mu = 36 and standard deviation sigma = 6e.None of the aboveTopic 5: Normal probability distribution & sampling distributionQ1 Which of the following is not a property of the normal distribution?a.It is bell-shaped.b.It is slightly skewed left.c.Its measures of central tendency are all identical.d.Its range is from negative infinity to positive infinity.Q2 The area under the standardized normal curve from 0 to 1.96 would be:a.the same as the area from 0 to -1.96.b.equal to 0.4750.c.found by using Table E.2 in your textbook.d.all of the above.Q3 Which of the following about the normal distribution is not true?a.Theoretically, the mean, median, and mode are the same.b.About two-thirds of the observations fall within ± 1 standard deviation from the mean.c.It is a discrete probability distribution.d.Its parameters are the mean and standard deviation.Q4 In its standardized form, the normal distribution:a.has a mean of 0 and a standard deviation of 1.b.has a mean of 1 and a variance of 0.c.has a total area equal to 0.5.d.cannot be used to approximate discrete binomial probability distributions.Q5 In the standardized normal distribution, the probability that Z > 0 is _______.a.0.00b.0.50c. 1.00d.cannot be found without more informationQ6 The probability of obtaining a value greater than 110 in a normal distribution in which the mean is 100 and the standard deviation is 10 is ______________ the probability of obtaining a value greater than 650 in a normal distribution with a mean of 500 and a standard deviation of 100.a.less thanb.equal to.c.greater thand.It is unknown without more information.Q7 The probability of getting a Z score greater than 4.0 is ________.a.close to 1.0b.0.50c. a negative numberd.almost zeroQ8 For some positive value of Z, the probability that a standardized normal variable is between 0 and Z is 0.3770. The value of Z isa.0.18b.0.81c. 1.16d. 1.47Q9 For some value of Z, the probability that a standardized normal variable is below Z is 0.2090. The value of Z isa.-0.81b.-0.31c.0.31d. 1.96Q10 Given that X is a normally distributed random variable with a mean of 50 and a standard deviation of 2, the probability that X is between 47 and 54 isa.0.0896b.0.4104c.0.5896d.0.9104Q11 For some positive value of X, the probability that a standardized normal variable is between 0 and +1.5X is 0.4332. The value of X isa.0.10b.0.50c. 1.00d. 1.50Q12 The owner of a fish market determined that the average weight for a catfish is 3.2 pounds with a standard deviation of 0.8 pounds. A citation catfish should be one of the top 2 percent in weight. Assuming the weights of catfish are normally distributed, at what weight (in pounds) should the citation designation be established?a. 1.56 poundsb. 4.84 poundsc. 5.20 poundsd.7.36 poundsQ13 Which of the following is NOT a property of the arithmetic mean?a.It is unbiased.b.It is always equal to the population mean.c.Its average is equal to the population mean.d.Its variance becomes smaller when the sample size gets bigger.Q14 The sampling distribution of the mean is a distribution of:a.individual population values.b.individual sample values.c.statistics.d.parameters.Q15 The standard deviation of the sampling distribution of the mean is called the:a.standard error of the sample.b.standard error of the estimate.c.standard error of the mean.d.All of the aboveQ16 According to the central limit theorem, the sampling distribution of the mean can be approximated by the normal distribution:a.as the number of samples gets "large enough."b.as the sample size (number of observations) gets "large enough."c.as the size of the population standard deviation increases.d.as the size of the sample standard deviation decreases.Q17 For a sample size of n=10, the sampling distribution of the mean will be normally distributed:a.regardless of the population's distribution.b.if the shape of the population is symmetrical.c.if the variance of the mean is known.d.if the population is normally distributedTopic 6: EstimationQ1 The interval estimate using the t critical value is ________ than the interval estimate using the z critical value.a.Narrowerb.The same asc.Widerd.More powerfulQ2 To estimate the mean of a normal population with unknown standard deviation using a small sample, we use the ______ distribution.a.'t'b.'Z'c.samplingd.alphaQ3 If the population does not follow a normal distribution, then to use the t distribution to give a confidence interval estimate for the population mean, the sample size should be:a.at least 5b.at least 30c.at least 100d.less than 30Q4 The 'z' value or 't' value used in the confidence interval formula is called the:a.sigma valueb.critical valuec.alpha valued.none of the aboveQ5 The 'z' value that is used to construct a 90 percent confident interval is:a. 1.645b. 1.96c. 2.33d. 2.58Q6 The 'z' value that is used to construct a 95 percent confidence interval is:a. 1.645b. 1.96c. 2.33d. 2.58Q7 The sample size needed to construct a 90 percent confidence interval estimate for the population mean with sampling error ±1.9 when sigma is known to be 10 units is:a.9b.32c.75d.107Q8 The t critical value approaches the z critical value when:a.the sample size decreasesb.the sample size approaches infinityc.the confidence level increasesd.the sample is smallQ9 The t-critical value used when constructing a 99 percent confidence interval estimate with a sample of size 18 is:a. 2.552b. 2.567c. 2.878d. 2.898Q10 The t-value that would be used to construct a 90 percent confidence interval for the mean with a sample of size n 36 would be:a. 1.3062b. 1.6499c. 1.6883d. 1.6896Q11 The value of alpha (two tailed) for a 96 percent confidence interval would be:a.0.02b.0.04c.0.2d.0.4Q12 When using the t distribution for confidence interval estimates for the mean, the degrees of freedom value is:a.nb.n-1c.n-2d.n %2B 1Q13 You would interpret a 90 percent confidence interval for the population mean as:a.you can be 90 percent confident that you have selected a sample whose interval doesinclude the population meanb.if all possible samples are selected and CI's are calculated, 90 percent of those intervalswould include the true population meanc.90 percent of the population is in that intervald.both A and B are trueQ14 From a sample of 100 items, 30 were defective. A 95 percent confidence interval for the proportion of defectives in the population is:a.(.2, .4)b.(.21, .39)c.(.225, .375)d.(.236, .364)Q15 A confidence interval was used to estimate the proportion of statistics students that are male.A random sample of 70 statistics students generated the following 90 percent confidence interval:(0.45, 0.64). Using the information above, what size sample would be necessary if we wanted to estimate the true proportion to within ±0.08 using 95 percent confidence?a.240b.450c.550d.150整理人：阿桤（注：可编辑下载，若有不当之处，请指正，谢谢!）。
计量经济学答案5-9章
计量经济学答案5-9章CHAPTER 5SOLUTIONS TO PROBLEMS5.1 Write y + x1 + u, and take the expected value: Ey? ?+ Ex1?+ Eu, or μy? ?+ μx since Eu 0, where μy Ey and μx? Ex1We can rewrite this as ? μy?- μxNow, ?Taking the plim of this we have plim? plim? ? plim?? plimplim? μy? μx, where we use the fact that plim? μy?and plim? μx by the law of large numbers, and plim? We have also used the parts of Property PLIM.2 from Appendix C.5.2 A higher tolerance of risk means more willingness to invest in the stock market, so ? 0By assumption, funds and risktol are positively correlatedNow we use equation 5.5, where 1? 0: plim? ?+ 1? , so has a positive inconsistency asymptotic biasThis makes sense: if we omit risktol from the regression and it is positively correlated with funds, some of the estimated effect of funds is actually due to the effect of risktol.5.3 The variable cigs has nothing close to a normal distribution in the populationMost people do not smoke, so cigs? 0 for over half of the populationA normally distributed random variable takes on no particular value with positive probabilityFurther, the distribution of cigs isskewed, whereas a normal random variable must be symmetric about its mean.5.4 Write y + x?+ u, and take the expected value: Ey? ?+ Ex?+ Eu, or μy? ?+ μx, since Eu? 0, where ?μy? Ey and μx? ExWe can rewrite this as ? μy? μxNow, ?Taking the plim of this we have plim? plim? ? plim?? plimplim? μy? μx, where we use the fact that plim? μy and plim? μx by the law of large numbers, and plim? We have also used the parts of the Property PLIM.2 from Appendix C.CHAPTER 6SOLUTIONS TO PROBLEMS6.1 The generality is not necessaryThe t statistic on roe2 is only about .30, which shows that roe2 is very statistically insignificantPlus, having the squared term has only a minor effect on the slope even for large values of roeThe approximate slope is .021500016 roe, and even when roe? 25?? about one standard deviation above the average roe in the sample ? the slope is .211, as compared with .215 at roe? 0.6.2 By definition of the OLS regression of c0yi on c1xi1, , ckxik, i? 2, , n, the solve [We obtain these from equations 3.13, where we plug in the scaled dependent and independent variables.] We now show that if ? and ? , j? 1,…,k, then the se k?+ 1 first order conditions are satisfied, which proves the result because we know that the OLS estimates are the unique solutions to the FOCs once we rule out perfect collinearity in the independent variablesPlugging in these guesses for the gives theexpressionsfor j 1,2,…,kSimple cancellation shows we can write these equations asandor, factoring out constants,and, j 1, 2,But the terms multiplying c0 and c0cj are identically zero by the first order conditions for the since, by definition, they are obtained from the regression yi on xi1, , xik, i? 1,2,..,nSo we have shown that ? c0 and ? c0/cj , j? 1, , k solve the requisite first order conditions.6.3 i The turnaround point is given by /2||, or .0003/.000000014? 21,428.57; remember, this is sales in millions of dollarsii ProbablyIts t statistic is about ?1.89, which is significant against the one-sided alternative H0: ? 0 at the 5% level cv? ?1.70 with df? 29In fact, the p-value is about .036iii Because sales gets divided by 1,000 to obtain salesbil, the corresponding coefficient gets multiplied by 1,000: 1,000.00030? .30The standard error gets multiplied by the same factorAs stated in the hint, salesbil2? sales/1,000,000, and so the coefficient on the quadratic gets multiplied by one million: 1,000,000.0000000070? .0070; its standard error also gets multiplied by one millionNothing happens to the intercept because rdintens has not been rescaled or to the R2:2.613 + .30 salesbil ? .0070 salesbil20.429.14.0037n 32, R2 .1484iv The equation in part iii is easier to read because it contains fewer zeros to the right of the decimalOf course the interpretation of the two equations is identical once the different scales are accounted for.6.4 i Holding all other factors fixed we haveDividing both sides by ?educ gives the resultThe sign of is not obvious, although ? 0 if we think a child gets more out of another year of education the more highly educated are the child’s parentsii We use the values pareduc? 32 and pareduc? 24 to interpret the coefficient on educpareducThe difference in the estimated return to education is .0007832?? 24? .0062, or about .62 percentage pointsiii When we add pareduc by itself, the coefficient on the interaction term is negativeThe t statistic on educpareduc is about ?1.33, which is not significant at the 10% level against a two-sided alternativeNote that the coefficient on pareduc is significant at the 5% level against a two-sided alternativeThis provides a good example of how omitting a level effect pareduc in this case can lead to biased estimation of the interaction effect.6.5 This would make little sensePerformances on math and science exams are measures of outputs of the educational process, and we would like to know how various educational inputs and school characteristics affect math and science scoresFor example, if the staff-to-pupil ratiohas an effect on both exam scores, why would we want to hold performance on the science test fixed while studying the effects of staff on the math pass rateThis would be an example of controlling for too many factors in a regression equationThe variable scill could be a dependent variable in an identical regression equation.6.6 The extended model has df? 680?? 9? 671, and we are testing two restrictionsTherefore, F? [.232?? .229/1?? .232]671/2? 1.31, which is well below the 10% critical value in the F distribution with 2 and df: cv? 2.30Thus, atndrte2 and ACTatndrte are jointly insignificantBecause adding these terms complicates the model without statistical justification, we would not include them in the final model.6.7 The second equation is clearly preferred, as its adjusted R-squared is notably larger than that in the other two equationsThe second equation contains the same number of estimated parameters as the first, and the one fewer than the thirdThe second equation is also easier to interpret than the third.6.8 i The answer is not entire obvious, but one must properly interpret the coefficient on alcohol in either caseIf we include attend, then we are measuring the effect of alcohol consumption on college GPA, holding attendance fixedBecause attendance is likely to be an important mechanism through which drinking affects performance, we probably do not want to hold it fixed in the analysisIf we do include attend, then we interpretthe estimate of as being those effects on colGPA that are not due to attending classFor example, we could be measuring the effects that drinking alcohol has on study time. To get a total effect of alcohol consumption, we would leave attend outii We would want to include SAT and hsGPA as controls, as these measure student abilities and motivationDrinking behavior in college could be correlated with one’s performance in high school and on standardized testsOther factors, such as family background, would also be good controls.CHAPTER 7SOLUTIONS TO PROBLEMS7.1 i The coefficient on male is 87.75, so a man is estimated to sleep almost one and one-half hours more per week than a comparable womanFurther, tmale? 87.75/34.33 2.56, which is close to the 1% critical value against a two-sided alternative about 2.58Thus, the evidence for a gender differential is fairly strongii The t statistic on totwrk is .163/.018 9.06, which is very statistically significantThe coefficient implies that one more hour of work 60 minutes is associated with .16360 9.8 minutes less sleepiii To obtain , the R-squared from the restricted regression, we need to estimate the model without age and age2When age and age2? are both in the model, age has no effect only if the parameters on both terms are zero.7.2 i If cigs? 10 then ? .004410? .044, which means about a 4.4%lower birth weightii A white child is estimated to weigh about 5.5% more, other factors in the first equation fixedFurther, twhite 4.23, which is well above any commonly used critical valueThus, the difference between white and nonwhite babies is also statistically significantiii If the mother has one more year of education, the child’s birth weight is estimated to be .3% higherThis is not a huge effect, and the t statistic is only one, so it is not statistically significantiv The two regressions use different sets of observationsThe second regression uses fewer observations because motheduc or fatheduc are missing for some observationsWe would have to reestimate the first equation and obtain the R-squared using the same observations used to estimate the second equation.7.3 i The t statistic on hsize2 is over four in absolute value, so there is very strong evidence that it belongs in the equationWe obtain this by finding the turnaround point; this is the value of hsize that imizes other things fixed: 19.3/22.19 4.41Because hsize is measured in hundreds, the optimal size of graduating class is about 441ii This is given by the coefficient on female since black? 0: nonblack females have SAT scores about 45 points lower than nonblack malesThe t statistic is about ?10.51, so the difference is very statistically significantThe very large sample size certainly contributes to the statistical significance.iii Because female? 0, the coefficient on black implies that a blackmale has an estimated SAT score almost 170 points less than a comparable nonblack maleThe t statistic is over 13 in absolute value, so we easily reject the hypothesis that there is no ceteris paribus differenceiv We plug in black? 1, female? 1 for black females and black? 0 and female?1 for nonblack femalesThe difference is therefore ?169.81?+ 62.31? 107.50Because the estimate depends on two coefficients, we cannot construct a t statistic from the information givenThe easiest approach is to define dummy variables for three of the four race/gender categories and choose nonblack females as the base groupWe can then obtain the t statistic we want as the coefficient on the black female dummy variable.7.4 i The approximate difference is just the coefficient on utility times 100, or ?28.3%The t statistic is .283/.099 2.86, which is very statistically significantii 100[exp.283?? 1 24.7%, and so the estimate is somewhat smaller in magnitudeiii The proportionate difference is .181158? .023, or about 2.3%One equation that can be estimated to obtain the standard error of this difference islogsalary + logsales + roe + consprod + utility +trans + u,where trans is a dummy variable for the transportation industryNow, the base group is finance, and so the coefficient directly measures the difference between the consumer products and finance industries, and we can use the t statistic on consprod.7.5 i Following the hint, + 1?? noPC?+ hsGPA?+ ACT? ?+ ? noPC?+hsGPA?+ ACTFor the specific estimates in equation 7.6, ? 1.26 and ? .157, so the new intercept is 1.26 + .157? 1.417The coefficient on noPC is ?.157ii Nothing happens to the R-squaredUsing noPC in place of PC is simply a different way of including the same information on PC ownershipiii It makes no sense to include both dummy variables in the regression: we cannot hold noPC fixed while changing PCWe have only two groups based on PC ownership so, in addition to the overall intercept, we need only to include one dummy variableIf we try to include both along with an intercept we have perfect multicollinearity the dummy variable trap.7.6 In Section 3.3 ? in particular, in the discussion surrounding Table 3.2?? we discussed how to determine the direction of bias in the OLS estimators when an important variable ability, in this case has been omitted from the regressionAs we discussed there, Table 3.2 only strictly holds with a single explanatory variable included in the regression, but we often ignore the presence of other independent variables and use this table as a rough guideOr, we can use the results of Problem 3.10 for a more precise analysis. If less able workers are more likely to receive training, then train and u are negatively correlatedIf we ignore the presence of educ and exper, or at least assume that train and u are negatively correlated after netting out educ and exper, then we can use Table 3.2: the OLS estimator of with ability in the error term has adownward biasBecause we think0, we are less likely to conclude that the training program was effectiveIntuitively, this makes sense: if those chosen for training had not received training, they would have lowers wages, on average, than the control group.7.7 i Write the population model underlying 7.29 asinlf + nwifeinc + educ + exper +exper2 + age+ kidslt6 + kidsage6 + u,plug in inlf? 1?? outlf, and rearrange:1 ? outlf + nwifeinc + educ + exper +exper2 + age+ kidslt6 + kidsage6 + u,oroutlf 1 nwifeinc educ experexper2 agekidslt6 kidsage6 u,The new error term, u, has the same properties as uFrom this we see that if we regress outlf on all of the independent variables in 7.29, the new intercept is 1586? .414 and each slope coefficient takes on the opposite sign from when inlf is the dependent variableFor example, the new coefficient on educ is .038 while the new coefficient on kidslt6 is .262ii The standard errors will not changeIn the case of the slopes, changing the signs of the estimators does not change their variances, and therefore the standard errors are unchanged but the t statistics change signAlso, Var1? ? Var, so the standard error of the intercept is the sameas beforeiii We know that changing the units of measurement of independent variables, or entering qualitative information using different sets of dummy variables, does not change the R-squaredBut here we are changing the dependent variableNevertheless, the R-squareds from the regressions are still the sameTo see this, part i suggests that the squared residuals will be identical in the two regressionsFor each i the error in the equation for outlfi is just the negative of the error in the other equation for inlfi, and the same is true of the residualsTherefore, the SSRs are the sameFurther, in this case, the total sum of squares are the sameFor outlf we haveSST ,which is the SST for inlfBecause R2? 1?? SSR/SST, the R-squared is the same in the two regressions.7.8 i We want to have a constant semi-elasticity model, so a standard wage equation with marijuana usage included would belogwage + usage + educ + exper + exper2 + female + u.Then 100 is the approximate percentage change in wage when marijuana usage increases by one time per monthii We would add an interaction term in female and usage:logwage + usage + educ + exper + exper2 + female+ femaleusage + u.The null hypothesis that the effect of marijuana usage does not differby gender is H0: ? 0iii We take the base group to be nonuserThen we need dummy variables for the other three groups: lghtuser, moduser, and hvyuserAssuming no interactive effect with gender, the model would belogwage + lghtuser + moduser + hvyuser + educ + exper + exper2 + female + uiv The null hypothesis is H0: 0, 0, 0, for a total of q? 3 restrictionsIf n is the sample size, the df in the unrestricted model ? the denominator df in the F distribution ? is n?? 8So we would obtain the critical value from the Fq,n-8 distributionv The error term could contain factors, such as family background including parental history of drug abuse that could directly affect wages and also be correlated with marijuana usageWe are interested in the effects of a person’s drug usage on his or her wage, so we would like to hold other confounding factors fixedWe could try to collect data on relevant background information.7.9 i Plugging in u 0 and d 1 givesii Setting gives or Therefore, provided , we have Clearly, is positive if and only if is negative, which means must have opposite signsiii Using part ii we have yearsiv The estimated years of college where women catch up to men is much too high to be practically relevantWhile the estimated coefficient on shows that the gap is reduced at higher levels of college, it is never closed ? not even closeIn fact, at four years of college, the difference in predicted log wage is still , or about 21.1% less for women.CHAPTER 8SOLUTIONS TO PROBLEMS8.1 Parts ii and iiiThe homoskedasticity assumption played no role in Chapter 5 in showing that OLS is consistentBut we know that heteroskedasticity causes statistical inference based on the usual t and F statistics to be invalid, even in large samplesAs heteroskedasticity is a violation of the Gauss-Markov assumptions, OLS is no longer BLUE.8.2 With Varu|inc,price,educ,female? 2inc2, hx? inc2, where hx is the heteroskedasticity function defined in equation 8.21Therefore, inc, and so the transformed equation is obtained by dividing the original equation by inc:Notice that , which is the slope on inc in the original model, is now a constant in the transformed equationThis is simply a consequence of the form of the heteroskedasticity and the functional forms of the explanatory variables in the original equation.8.3 FalseThe unbiasedness of WLS and OLS hinges crucially on Assumption MLR.4, and, as we know from Chapter 4, this assumption is often violated when an important variable is omittedWhen MLR.4 does not hold, both WLS and OLS are biasedWithout specific information on how the omitted variable is correlated with the included explanatory variables, it is not possible to determine which estimator has a small biasIt is possible that WLS would have more bias than OLS or less biasBecause we cannot know, we should not claim to use WLS in order to solve “biases” associated withOLS.8.4 i These coefficients have the anticipated signsIf a studenttakes courses where grades are, on average, higher ? as reflected by hig。
《Business statistic》复习参考(第一章至第八章)讲解学习
《Business Statistic》中国人民大学出版社英文版第五版chapter1~8复习参考Part1名词解释1、Statistics is a method of extracting useful information from a set of numerical data in order tomake a more effective and informed decision.2、Descriptive Statistics：These are statistical methods of organizing, summarizing andpresenting numerical data in convenient forms such as graphs, charts and tables.3、Inferential statistics is defined as statistical methods used for drawing conclusions about apopulation based on samples.4、Primary data is obtained first hand.5、Secondary data already exists or has been previously collected such as company accounts, orsales figures.6、Mean: The arithmetic average and the most common measure ofaaaaaaa central tendency. ①All values are included in computing the mean.②A set of data has a unique mean ③The mean is affected by unusually large or small data points (outliers / extreme values).7、Mode: The most frequent data, or data corresponding to the highest frequency. ①Mode is notaffected by extreme values. ②There may not be a mode. ③There may be several modes. ④Used for either numerical or categorical data.8、Median is the value that splits a ranked set of data into two equal parts. ①Median is notaffected by extremely large or small values and is therefore a valuable measure of central tendency when such values occur.9、Standard Deviation: ①A measure of the variation of data from the mean. ②The mostcommonly used measure of variation. ③Represented by the symbol ‘s’. ④Shows how the data is distributed around the mean.10、Probability is the chance of an occurrence of an event. ①Probability of an eventalways lies between 0 and 1. ②The sum of the probabilities of every possible outcome or event is 1. ③The probability of the complement A’ is given by 1-P(A).11、Properties of Normal distribution：①Continuous random variable. ②‘Bell-shaped’ &symmetrical. ③Mean, median, mode are equal ④Area under the curve is 1.12、The Central Limited Theorem：①If the population followed normal distribution, thesampling distribution of mean is followed normal distribution. ②If the population do not followed normal distribution, but the sample size is larger than 30, the sampling distribution of mean is followed normal distribution.Part2选择题Topic 1 - Introduction to Business Statistics & Data CollectionQ1. The universe or totality of items or things under consideration is called:a. a sample.b. a population.c. a parameter.d.none of the above.Q2. Those methods involving the collection, presentation, and characterization of a set of data in order to properly describe the various features of that set of data are called:a.inferential statistics.b.total quality management.c.sampling.d.descriptive statistics.Q3. The portion of the universe that has been selected for analysis is called:a. a sample.b. a frame.c. a parameter.d. a statistic.Q4. A summary measure that is computed to describe a numerical characteristic from only a sample of the population is called:a. a parameter.b. a census.c. a statistic.d.the scientific method.Q5. A summary measure that is computed to describe a characteristic of an entire population is called:a. a parameter.b. a census.c. a statistic.d.total quality management.Q6. The process of using sample statistics to draw conclusions about population parameters is called:a.inferential statistics.b.experimentation.c.primary sources.d.descriptive statistics.Q7. Which of the four methods of data collection is involved when a person retrieves data from an online database?a.published sources.b.experimentation.c.surveying.d.observation.Q8. Which of the four methods of data collection is involved when people are asked to complete a questionnaire?a.published sources.b.experimentation.c.surveying.d.observation.Q9. Which of the four methods of data collection is involved when a person records the use of the Los Angeles freeway system?a.published sources.b.experimentation.c.surveying.d.observation.Q10. A focus group is an example of which of the four methods of data collection?a.published sources.b.experimentation.c.surveying.d.observation.Q11. Which of the following is true about response rates?a.The longer the questionnaire, the lower the rate.b.Mail surveys usually produce lower response rates than personal interviews or telephonesurveys.c.Question wording can affect a response rate.d. d. All of the above.Q12. Which of the following is a reason that a manager needs to know about statistics?a.To know how to properly present and describe information.b.To know how to draw conclusions about the population based on sample information.c.To know how to improve processes.d.All of the above.Scenario 1-1Questions 13-15 refer to this scenario:An insurance company evaluates many variables about a person before deciding on an appropriate rate for automobile insurance. Some of these variables can be classified as categorical, discrete and numerical, or continuous and numerical.Q13. Referring to Scenario 1-1 (above), the number of claims a person has made in the last three years is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q14. Referring to Scenario 1-1 (above), a person's age is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q15. Referring to Scenario 1-1 (above), a person's gender is what type of variable?a.Categorical.b.Discrete and numerical.c.Continuous and numerical.d.None of the above.Q16. Which of the following can be reduced by proper interviewer training?a.Sampling error.b.Measurement error.c.Coverage error.d.Nonresponse error.Scenario 1-2Questions 17-19 refer to this scenario:Mediterranean fruit flies were discovered in California a few years ago and badly damaged the oranges grown in that state. Suppose the manager of a large farm wanted to study the impact of the fruit flies on the orange crops on a daily basis over a 6-week period. On each day a random sample of orange trees was selected from within a random sample of acres. The daily average number of damaged oranges per tree and the proportion of trees having damaged oranges were calculated.Q17. Referring to Scenario 1-2 (above), the two main measures calculated each day (i.e., average number of damaged oranges per tree and proportion of trees having damaged oranges) are called _______.a.statistics.b.parameters.c.samples.d.populations.Q18. Referring to Scenario 1-2 (above), the two main measures calculated each day (i.e., average number of damaged oranges per tree and proportion of trees having damaged oranges) may be used on a daily basis to estimate the respective true population _______.a.estimates.b.parameters.c.statistics.d.frame.Q19. Referring to Scenario 1-2 (above), in this study, drawing conclusions on any one day about the true population characteristics based on information obtained from the sample is called _______.a.evaluation.b.descriptive statistics.c.inferential statistics.d.survey.Scenario 1-3Questions 20 and 21 refer to this scenario:The Quality Assurance Department of a large urban hospital is attempting to monitor and evaluate patient satisfaction with hospital services. Prior to discharge, a random sample of patients is asked to fill out a questionnaire to rate such services as medical care, nursing, therapy, laboratory, food, and cleaning. The Quality Assurance Department prepares weekly reports that are presented at the Board of Directors meetings and extraordinary/atypical ratings are easy to flag.Q20. Referring to Scenario 1-3 (above), true population characteristics estimated from the sample results each week are called _____________.a.inferences.b.parameters.c.estimates.d.data.Q21. Referring to Scenario 1-3 (above), a listing of all hospitalised patients in this institution over a particular week would constitute the ________.a.sample.b.population.c.statistics.d.parameters.Scenario 1-4Questions 22-24 refer to this scenario:The following are the questions given to Sheila Drucker-Ferris in her college alumni association survey. Each variable can be classified as categorical or numerical, discrete or continuous.Q22. Referring to Scenario 1-4 (above), the data for the number of years since graduation is categorised as: __________________.a.numerical discrete.b.categorical.c.numerical continuous.d.none of the above.Q23. Referring to Scenario 1-4 (above), the data for the number of science majors is categorised as: ____________.a.categorical.b.numerical continuous.c.numerical discrete.d.none of the above.Q24. Referring to Scenario 1-4 (above), the data for tabulating the level of job satisfaction (High, Moderate, Low) is categorised as: _________.a.numerical continuous.b.categorical.c.numerical discrete.d.none of the above.Topic 2: Organising and Presenting dataQ1 The width of each bar in a histogram corresponds to the:a.boundaries of the classes.b.number of observations in the classes.c.midpoint of the classes.d.percentage of observations in the classes.Q2 When constructing charts, which of the following chart types is plotted at the class midpoints?a.Frequency histograms.b.Percentage polygons.c.Cumulative relative frequency ogives.d.Relative frequency histograms.Q3 When polygons or histograms are constructed, which axis must show the true zero or "origin"?a.The horizontal axis.b.The vertical axis.c.Both the horizontal and vertical axes.d.Neither the horizontal nor the vertical axis.Q4 To determine the appropriate width of each class interval in a grouped frequency distribution, we:a.divide the range of the data by the number of desired class intervals.b.divide the number of desired class intervals by the range of the datac.take the square root of the number of observations.d.take the square of the number of observations.Q5 When grouping data into classes it is recommended that we have:a.less than 5 classes.b.between 5 and 15 classes.c.more than 15 classes.d.between 10 and 30 classes.Q6 Which of the following charts would give you information regarding the number of observations "up to and including" a given group?a.Frequency histograms.b.Polygons.c.Percentage polygons.d.Cumulative relative frequency ogives.Q7 Another name for an "ogive" is a:a.frequency histogram.b.polygon.c.percentage polygon.d.cumulative percentage polygon.Q8 In analyzing categorical data, the following graphical device is NOT appropriate:a.bar chart.b.Pareto diagram.c.stem and leaf display.d.pie chart.Table 2The opinions of a sample of 200 people broken down by gender about the latest congressional For Neutral Against Totals Female 38 54 12 104Male 12 36 48 96Q9 Table 2 (above) contains the opinions of a sample of 200 people broken down by gender about the latest congressional plan to eliminate anti-trust exemptions for professional baseball. Referring to Table 2, the number of people who are neutral to the plan is _______.a.36b.54c.90d.200Q10 Referring to Table 2, the number of males who are against the plan is _______.a.12b.48c.60d.96Q11 Referring to Table 2, the percentage of males among those who are for the plan is ______.a.12.5%b.24%c.25%d.76%Q12 Referring to Table 2, the percentage who are against the plan among the females is _______.a.11.54%b.20%c.30%d.52%Topic 3: Numerical Descriptive StatisticsQ1 Which measure of central tendency can be used for both numerical and categorical variables?a.Mean.b.Median.c.Mode.d.Quartiles.Q2 Which of the following statistics is not a measure of central tendency?a.Mean.b.Median.c.Mode.d.Q3.Q3 Which of the following statements about the median is NOT true?a.It is more affected by extreme values than the mean.b.It is a measure of central tendency.c.It is equal to Q2.d.It is equal to the mode in bell-shaped distributions.Q4 The value in a data set that appears most frequently is called:a.the median.b.the mode.c.the mean.d.the variance.Q5 In a perfectly symmetrical distribution:a.the mean equals the median.b.the median equals the mode.c.the mean equals the mode.d.All of the above.Q6 When extreme values are present in a set of data, which of the following descriptive summary measures are most appropriate?a.CV and range.b.Mean and standard deviation.c.Median and interquartile range.d.Mode and variance.Q7 The smaller the spread of scores around the mean:a.the smaller the interquartile range.b.the smaller the standard deviation.c.the smaller the coefficient of variation.d.All the above.Q8 In a right-skewed distribution:a.the median equals the mean.b.the mean is less than the median.c.the mean is greater than the median.d.the mean is less than the mode.Q9 Referring to Table 3 (above), the mean carbohydrates in this sample is ________ grams.a.15.25b.19.73c.21.42d.21.70Q10 Referring to Table 3 (above), the median carbohydrate amount in the cereal is ________ grams.a.19b.20c.21d.21.5Q11 Referring to Table 3 (above), the 1st quartile of the carbohydrate amounts is ________ grams.a.15b.20c.21d.25Q12 Referring to Table 3 (above), the range in the carbohydrate amounts is ________ grams.a.16b.18c.20d.21Topic 4: Basics probability and discrete probability distributionsInformation A, needed to answer Questions 1 to 2The Health and Safety committee in a large retail firm is examining the relationship between the number of days of sick leave an employee takes and whether an employee works on the day shift (D) or night shift (N). The committee looks at a sample of 50 employees and notes which shift they work on and whether the number of days of sick leave they take in a year is less than 6 daysQ1 Use Information A to answer this question. Which of the following statements about the values in the table of probabilities is not correct?a.The probability of an employee taking 6 or more days of sick leave P(M) is 0.6b.The probability that an employee is on the Night Shift (N) and takes less than 6 days ofleave (L), is called a conditional probability P(N | L) = 0.6c.If you know that an employee is on day shift (D) then the probability that they will takeless than 6 days of leave (L) is the conditional probability P(L | D) = 0.4d.The probability that an employee works Day Shift (D) or takes 6 or more days of leave(M) is found using the addition rule to be P(D or M) = 0.76e.They are all correctQ2 The analyst wishes to use the Probabilities table from Information A to determine whether the work shift variable and the number of days of sick leave variable are or are not independent variables. Which of the following statements about the work shift and the number of days of sickleave variables is correct ?a.These variables are independent because the marginal probabilities such as P(L) are thesame as the conditional probabilities P(L | D)b.These variables are not independent because the marginal probability P(L) is differentfrom the conditional probability P(N | L)c.These variables are not independent because the joint probabilities such as P(L and N) areequal to the product of the probabilities P(L).P(N).d.These variables are dependent because the marginal probabilities such as P(L) are equalto the conditional probability P(L | N)e.None of the aboveInformation B, needed to answer Question 3Suppose the manager of a home ware retailer decides in a 5-minute period no more than 4 customers can arrive at a counter. Using past records he obtains the following probabilityTable 4-3Arrivals (X) 0 1 2 3 4P(X) .15 .20 .30 .20 .15Q3 Use Information B to answer this question. If values are rounded to 3 decimal places which of the following is the correct pair of values for the mean, the variance or standard deviation of the number of arrivals at the counter.a.Mean mu = 2 and variance sigma-squared = 1.265b.Mean mu = 2.5 and variance sigma-squared = 1.6c.Mean mu = 2 and standard deviation sigma = 1.6d.Mean mu = 2.4 and variance sigma-squared = 1.6e.None of the aboveInformation C, needed to answer Questions 4-6The section manager in an insurance company is interested in evaluating how well staff at the inquiry counter handle customer complaints. She interviews a sample of n = 6 customers who have made complaints and asks each of them whether staff had handled their complaints well. Each interview is called a trial. If a customer says their complaint was handled well this is called a success. She thinks that as long as these people are interviewed independently of each other then the number of people who say their complaint was handled well is a random variable with a Binomial probability distribution. The section manager thinks that the probability that a customers complaint will be handled well is p = 0.75.Q4 Use Information C to answer this question. A total of n = 6 people are interviewed independently of each other. Which of the following statements about the probability that 5 out of the 6 complaints will be handled well is correcta.less than 0.06b.between 0.23 and 0.24c.more than 0.35d.between 0.30 and 0.32e.None of the aboveQ5 Using Information C, which of the following statements about the probability that 4 or less of the 6 complaints will be handled well is correcta.less than 0.36b.more than 0.52c.between 0.45 and 0.475d.between 0.15 and 0.175e.None of the aboveQ6 Suppose the section manager from Information C is interested in the measures of central tendency and variation for the number of complaints which are handled well. Which of the following sets of values, where values are rounded to 3 decimal places, is the correct set of valuesa.Mean mu = 4.5 and variance sigma-squared = 1.125b.Mean mu = 4.5 and variance sigma-squared = 1.061c.Mean mu = 1.5 and variance sigma-squared = 1.125d.Mean mu = 1.5 and standard deviation sigma = 1.061e.None of the aboveInformation D, needed to answer Questions 7-9The manager of a large retailer thinks that one reason why staff at the complaints counter fail to handle customer complaints well is that not enough staff are allocated to this counter. Past experience has shown that the number of customers who arrive at this counter has a Poisson distribution where the average number who arrive each hour is 36. He decides to look at how many customers are likely to arrive at the complaints counter during a 5-minute period.Q7 Use Information D to answer this question. Which of the following statements concerning the probability that exactly 2 customers will arrive at the counter in a 5-minute period is correcta.less than 0.05b.between 0.21 and 0.23c.between 0.16 and 0.18d.more than 0.25e.None of the aboveQ8 Use Information D to answer this question. Which of the following statements concerning the probability that 3 or more customers will arrive at a counter in a 5-minute period is correcta.between 0.10 and 0.15b.less than 0.23c.more than 0.77d.between 0.60 and 0.55e.None of the aboveQ9 The section manager from Information D is interested in the mean and variance of the number of customers who arrive during a 1 hour period. Which of the following is the correct set of values for these two measuresa.Mean mu = 3 and variance sigma-squared = 3b.Mean mu = 36 and standard deviation sigma = 1.732c.Mean mu = 30 and variance sigma-squared = 30d.Mean mu = 36 and standard deviation sigma = 6e.None of the aboveTopic 5: Normal probability distribution & sampling distributionQ1 Which of the following is not a property of the normal distribution?a.It is bell-shaped.b.It is slightly skewed left.c.Its measures of central tendency are all identical.d.Its range is from negative infinity to positive infinity.Q2 The area under the standardized normal curve from 0 to 1.96 would be:a.the same as the area from 0 to -1.96.b.equal to 0.4750.c.found by using Table E.2 in your textbook.d.all of the above.Q3 Which of the following about the normal distribution is not true?a.Theoretically, the mean, median, and mode are the same.b.About two-thirds of the observations fall within ± 1 standard deviation from the mean.c.It is a discrete probability distribution.d.Its parameters are the mean and standard deviation.Q4 In its standardized form, the normal distribution:a.has a mean of 0 and a standard deviation of 1.b.has a mean of 1 and a variance of 0.c.has a total area equal to 0.5.d.cannot be used to approximate discrete binomial probability distributions.Q5 In the standardized normal distribution, the probability that Z > 0 is _______.a.0.00b.0.50c. 1.00d.cannot be found without more informationQ6 The probability of obtaining a value greater than 110 in a normal distribution in which the mean is 100 and the standard deviation is 10 is ______________ the probability of obtaining a value greater than 650 in a normal distribution with a mean of 500 and a standard deviation of 100.a.less thanb.equal to.c.greater thand.It is unknown without more information.Q7 The probability of getting a Z score greater than 4.0 is ________.a.close to 1.0b.0.50c. a negative numberd.almost zeroQ8 For some positive value of Z, the probability that a standardized normal variable is between 0 and Z is 0.3770. The value of Z isa.0.18b.0.81c. 1.16d. 1.47Q9 For some value of Z, the probability that a standardized normal variable is below Z is 0.2090. The value of Z isa.-0.81b.-0.31c.0.31d. 1.96Q10 Given that X is a normally distributed random variable with a mean of 50 and a standard deviation of 2, the probability that X is between 47 and 54 isa.0.0896b.0.4104c.0.5896d.0.9104Q11 For some positive value of X, the probability that a standardized normal variable is between 0 and +1.5X is 0.4332. The value of X isa.0.10b.0.50c. 1.00d. 1.50Q12 The owner of a fish market determined that the average weight for a catfish is 3.2 pounds with a standard deviation of 0.8 pounds. A citation catfish should be one of the top 2 percent in weight. Assuming the weights of catfish are normally distributed, at what weight (in pounds) should the citation designation be established?a. 1.56 poundsb. 4.84 poundsc. 5.20 poundsd.7.36 poundsQ13 Which of the following is NOT a property of the arithmetic mean?a.It is unbiased.b.It is always equal to the population mean.c.Its average is equal to the population mean.d.Its variance becomes smaller when the sample size gets bigger.Q14 The sampling distribution of the mean is a distribution of:a.individual population values.b.individual sample values.c.statistics.d.parameters.Q15 The standard deviation of the sampling distribution of the mean is called the:a.standard error of the sample.b.standard error of the estimate.c.standard error of the mean.d.All of the aboveQ16 According to the central limit theorem, the sampling distribution of the mean can be approximated by the normal distribution:a.as the number of samples gets "large enough."b.as the sample size (number of observations) gets "large enough."c.as the size of the population standard deviation increases.d.as the size of the sample standard deviation decreases.Q17 For a sample size of n=10, the sampling distribution of the mean will be normally distributed:a.regardless of the population's distribution.b.if the shape of the population is symmetrical.c.if the variance of the mean is known.d.if the population is normally distributedTopic 6: EstimationQ1 The interval estimate using the t critical value is ________ than the interval estimate using the z critical value.a.Narrowerb.The same asc.Widerd.More powerfulQ2 To estimate the mean of a normal population with unknown standard deviation using a small sample, we use the ______ distribution.a.'t'b.'Z'c.samplingd.alphaQ3 If the population does not follow a normal distribution, then to use the t distribution to give a confidence interval estimate for the population mean, the sample size should be:a.at least 5b.at least 30c.at least 100d.less than 30Q4 The 'z' value or 't' value used in the confidence interval formula is called the:a.sigma valueb.critical valuec.alpha valued.none of the aboveQ5 The 'z' value that is used to construct a 90 percent confident interval is:a. 1.645b. 1.96c. 2.33d. 2.58Q6 The 'z' value that is used to construct a 95 percent confidence interval is:a. 1.645b. 1.96c. 2.33d. 2.58Q7 The sample size needed to construct a 90 percent confidence interval estimate for the population mean with sampling error ±1.9 when sigma is known to be 10 units is:a.9b.32c.75d.107Q8 The t critical value approaches the z critical value when:a.the sample size decreasesb.the sample size approaches infinityc.the confidence level increasesd.the sample is smallQ9 The t-critical value used when constructing a 99 percent confidence interval estimate with a sample of size 18 is:a. 2.552b. 2.567c. 2.878d. 2.898Q10 The t-value that would be used to construct a 90 percent confidence interval for the mean with a sample of size n 36 would be:a. 1.3062b. 1.6499c. 1.6883d. 1.6896Q11 The value of alpha (two tailed) for a 96 percent confidence interval would be:a.0.02b.0.04c.0.2d.0.4Q12 When using the t distribution for confidence interval estimates for the mean, the degrees of freedom value is:a.nb.n-1c.n-2d.n %2B 1Q13 You would interpret a 90 percent confidence interval for the population mean as:a.you can be 90 percent confident that you have selected a sample whose interval doesinclude the population meanb.if all possible samples are selected and CI's are calculated, 90 percent of those intervalswould include the true population meanc.90 percent of the population is in that intervald.both A and B are trueQ14 From a sample of 100 items, 30 were defective. A 95 percent confidence interval for the proportion of defectives in the population is:a.(.2, .4)b.(.21, .39)c.(.225, .375)d.(.236, .364)Q15 A confidence interval was used to estimate the proportion of statistics students that are male.A random sample of 70 statistics students generated the following 90 percent confidence interval:(0.45, 0.64). Using the information above, what size sample would be necessary if we wanted to estimate the true proportion to within ±0.08 using 95 percent confidence?a.240b.450c.550d.150整理人：阿桤。
统计学术语中英文对照
population 母体sample 样本census 普查sampling 抽样quantitative 量的qualitative/categorical质的discrete 离散的continuous 连续的population parameters 母体参数sample statistics 样本统计量descriptive statistics 叙述统计学inferential/inductive statistics 推论 ...抽样调查（sampliing survey单纯随机抽样（simple random sampling 系统抽样（systematic sampling分层抽样（stratified sampling整群抽样（cluster sampling多级抽样（multistage sampling常态分配(Parametric Statistics)无母数统计学(Nonparametric Statistics) 实验设计(Design of Experiment)参数(Parameter)Data analysis 资料分析Statistical table 统计表Statistical chart 统计图Pie chart 圆饼图Stem-and-leaf display 茎叶图Box plot 盒须图Histogram 直方图Bar Chart 长条图Polygon 次数多边图Ogive 肩形图Descriptive statistics 叙述统计学Expectation 期望值Mode 众数Mean 平均数Variance 变异数Standard deviation 标准差Standard error 标准误Covariance matrix 共变异数矩阵Inferential statistics 推论统计学Point estimation 点估计Interval estimation 区间估计Confidence interval 信赖区间Confidence coefficient 信赖系数Testing statistical hypothesis 统计假设检定Regression analysis 回归分析Analysis of variance 变异数分析Correlation coefficient 相关系数Sampling survey 抽样调查Census 普查Sampling 抽样Reliability 信度Validity 效度Sampling error 抽样误差Non-sampling error 非抽样误差Random sampling 随机抽样Simple random sampling 简单随机抽样法Stratified sampling 分层抽样法Cluster sampling 群集抽样法Systematic sampling 系统抽样法Two-stage random sampling 两段随机抽样法Convenience sampling 便利抽样Quota sampling 配额抽样Snowball sampling 雪球抽样Nonparametric statistics 无母数统计The sign test 等级检定Wilcoxon signed rank tests 克森讯号等级检定Wilcoxon rank sum tests 克森等级和检定Run test 连检定法Discrete uniform densities 离散的均匀密度Binomial densities 二项密度Hypergeometric densities 超几何密度Poisson densities 卜松密度Geometric densities 几何密度Negative binomial densities 负二项密度Continuous uniform densities 连续均匀密度Normal densities 常态密度Exponential densities 指数密度Gamma densities 伽玛密度Beta densities 贝他密度Multivariate analysis 多变量分析Principal components 主因子分析Discrimination analysis 区别分析Cluster analysis 群集分析Factor analysis 因素分析Survival analysis 存活分析Time series analysis 时间序列分析Linear models 线性模式Quality engineering 品质工程Probability theory 机率论Statistical computing 统计计算Statistical inference 统计推论Stochastic processes 随机过程Decision theory 决策理论Discrete analysis 离散分析Mathematical statistics 数理统计统计学: Statistics母体: Population样本: Sample资料分析: Data analysis统计表: Statistical table统计图: Statistical chart圆饼图: Pie chart茎叶图: Stem-and-leaf display盒须图: Box plot直方图: Histogram长条图: Bar Chart次数多边图: Polygon肩形图: Ogive叙述统计学: Descriptive statistics 期望值: Expectation众数: Mode平均数: Mean变异数: Variance标准差: Standard deviation标准误: Standard error共变异数矩阵: Covariance matrix推论统计学: Inferential statistics点估计: Point estimation区间估计: Interval estimation信赖区间: Confidence interval信赖系数: Confidence coefficient统计假设检定: Testing statisticalhypothesis回归分析: Regression analysis变异数分析: Analysis of variance相关系数: Correlation coefficient抽样调查: Sampling survey普查: Census抽样: Sampling信度: Reliability效度: Validity抽样误差: Sampling error非抽样误差: Non-sampling error随机抽样: Random sampling简单随机抽样法: Simple randomsampling分层抽样法: Stratified sampling群集抽样法: Cluster sampling系统抽样法: Systematic sampling两段随机抽样法: Two-stage randomsampling便利抽样: Convenience sampling配额抽样: Quota sampling雪球抽样: Snowball sampling无母数统计: Nonparametric statistics等级检定: The sign test克森讯号等级检定: Wilcoxon signed ranktests克森等级和检定: Wilcoxon rank sumtests连检定法: Run test离散的均匀密度: Discrete uniformdensities二项密度: Binomial densities超几何密度: Hypergeometric densities卜松密度: Poisson densities几何密度: Geometric densities负二项密度: Negative binomial densities连续均匀密度: Continuous uniformdensities常态密度: Normal densities指数密度: Exponential densities伽玛密度: Gamma densities贝他密度: Beta densities多变量分析: Multivariate analysis主因子分析: Principal components区别分析: Discrimination analysis群集分析: Cluster analysis因素分析: Factor analysis存活分析: Survival analysis时间序列分析: Time series analysis线性模式: Linear models品质工程: Quality engineering机率论: Probability theory统计计算: Statistical computing统计推论: Statistical inference随机过程: Stochastic processes决策理论: Decision theory离散分析: Discrete analysis数理统计: Mathematical statistics统计名词市调辞典众数(Mode) 普查(census)指数(Index) 问卷(Questionnaire)中位数(Median) 信度(Reliability)百分比(Percentage) 母群体(Population)信赖水准(Confidence level) 观察法(Observational Survey)假设检定(Hypothesis Testing) 综合法(Integrated Survey)卡方检定(Chi-square Test) 雪球抽样(Snowball Sampling)差距量表(Interval Scale) 序列偏差(Series Bias)类别量表(Nominal Scale) 次级资料(Secondary Data)顺序量表(Ordinal Scale) 抽样架构(Sampling frame)比率量表(Ratio Scale) 集群抽样(Cluster Sampling)连检定法(Run Test) 便利抽样(Convenience Sampling)符号检定(Sign Test) 抽样调查(Sampling Sur)算术平均数(Arithmetic Mean) 非抽样误差(non-sampling error)展示会法(Display Survey)调查名词准确效度(Criterion-RelatedValidity)元素(Element) 邮寄问卷法(Mail Interview)样本(Sample) 信抽样误差(Sampling error)效度(Validity) 封闭式问题(Close Question)精确度(Precision) 访问法(TelephoneInterview)准确度(Validity) 随机抽样法(RandomSampling)实验法(Experiment Survey)抽样单位(Sampling unit) 资讯名词市场调查(Marketing Research) 决策树(Decision Trees)容忍误差(Tolerated erro) 资料采矿(DataMining)初级资料(Primary Data) 时间序列(Time-Series Forecasting)目标母体(Target Population) 回归分析(Regression)抽样偏差(Sampling Bias) 趋势分析(TrendAnalysis)抽样误差(sampling error) 罗吉斯回归(Logistic Regression)架构效度(Construct Validity) 类神经网络(Neural Network)配额抽样(Quota Sampling) 无母数统计检定方法(Non-Parametric Test)人员访问法(Interview) 判别分析法(Discriminant Analysis)集群分析法(cluster analysis) 规则归纳法(Rules Induction)容效度(Content Validity) 判断抽样(Judgment Sampling)开放式问题(Open Question) OLAP(OnlineAnalytical Process)分层随机抽样(Stratified Randomsampling) 资料仓储(Data Warehouse)非随机抽样法(Nonrandom Sampling) 知识发现(Knowledge DiscoveryAbsolute 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, 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MSC（多元散射校正）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 P-P, 正态概率分布图Normal Q-Q, 正态概率单位分布图Normal ranges, 正常围Normal value, 正常值Normalization 归一化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Pareto, 直条构成线图（又称佩尔托图）Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式PCA（主成分分析）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, 构成图,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PLS（偏最小二乘法）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 axis factoring,主轴因子法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 randomsampling, 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residual variance (剩余方差)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Sequence, 普通序列图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Significant Level, 显著水平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。
生物统计学名词术语 英汉对照
统计学 Statistics生物统计学 Biostatistics, Biometry总体(population)个体(individual)样本(sample)样本容量 (sample size)随机抽样(random sampling)参数(parameter)统计量(statistic)准确性(accuracy)精确性(precision)随机误差(random error)抽样误差 (sampling error)系统误差(systematic error)数量性状(quantitative character)数量性状资料 ( data of quantitative characteristics)质量性状(qualitative character)半定量或等级资料 (semi-quantitative or ranked data)全距又称为极差(range)长条图 (bar chart) 、园图(pie chart) 、线图(linear chart) 、直方图(histogram)和折线图 (broken-line chart)算术平均数（arithmetic mean）中位数（median）众数（mode）几何平均数（geometric mean）调和平均数（harmonic mean）变异系数coefficient of variance，记为C·V必然现象（inevitable phenomena）或确定性现象（definite phenomena）。
随机现象（random phenomena ）或不确定性现象（indefinite phenomena）随机试验（random trial）随机事件（random event），简称事件(event）必然事件（certain event）不可能事件（impossible event）概率（probability）频率（frequency）统计概率（statistics probability）连续性随机变量continuous random variable第1页共 4 页标准正态离差standard normal deviate二项分布binomial distribution正态分布 normal distribution 记为x～N(μ,σ2)标准正态分布(standard normal distribution波松分布(Poisson‘s distribution)，记为 x～P(λ)标准误 standard error假设检验 test of hypothesis参数估计parametric estimation显著性检验 test of significance区间估计 interval estimation处理效应 treatment effect无效假设 null hypothesis备择假设 alternative hypothesis显著水平significance levelⅠ型错误 type Ⅰ errorⅡ型错误 type Ⅱ error双侧检验 two-sided test，也叫双尾检验two-tailed test单侧检验（one-sided test）也叫单尾检验（one-tailed test）点估计（point estimation）和区间估计（interval estimation）置信区间（confidence interval）置信概率（confidence probability）方差分析(analysis of variance)试验指标(experimental index)试验因素(experimental factor)因素水平(level of factor)试验处理(experimental treatment)试验单元（experimental unit）重复(repetition)单因素完全随机设计（Completely Randomized Design with Single Factor ) 随机区组设计 Randomized Complete Block Design拉丁方设计Latin square design正交设计Orthogonal design第2页共 4 页效应的可加性（additivity）分布的正态性（normality）方差的同质性（homogeneity）期望均方，简记为EMS（expected mean squares）多重比较(multiple comparison)最小显著差数法 (LSD法，least significant difference) 最小显著极差法(LSR法 ,Least significant ranges)新复极差法(new multiple range method)简单效应(simple effect)平方根转换 (square root transformation)对数转换 (logarithmic transformation)反正弦转换 (arcsine transformation)回归分析（regression analysis）相关分析 ( correlation analysis)决定系数（coefficient of determination）相关系数（coefficient of correlation）第3页共 4 页。
经典六西格玛(6 sigma)培训内部资料D_03_Basic Statistic-13
5) Time used in inputting expenditure 输入支出费用的时间
Introduction to Six Sigma 7
Answers to Exercise答案
Type of Data (V or A)
1) Average expenditure / Phone On hold Time
顾客中 顾客平均消费,电话待机时间： V
2) S/W conflicts raised up because of Zip Drive
Zip drive 中发生 S/W 冲突
A
3) Employee 职员-
Tom, Nancy, Howard.
Introduction to Six Sigma 16
: . :: : : .:..:..:. +---+---+
Discrete Data vs Continuous Data 离散数据与连续数据
The Advantage of Continuous Data 连续数据的优点
Discrete离散= Sparse Information稀少的信息
sample sizes.
能够为使用相对小范围抽样的过程提供详细的信息
2、Can be used at low defect rates.
适用于低缺陷率
3、Can predict trends and future conditions. 能够预估发展趋势和情况
缺点 Disadvantages： 1、Often more difficult to get the data. 2、Analysis is more complex.
统计学基础专业词汇
population---总体sampling unit---抽样单元sample---样本observed value---观测值descriptive statistics---描述性统计量random sample---随机样本simple random sample---简单随机样本statistics---统计量order statistic---次序统计量sample range---样本极差mid-range---中程数estimator---估计量sample median---样本中位数sample moment of order k---k阶样本矩sample mean---样本均值average---平均数arithmetic mean---算数平均值sample variance---样本方差sample standard deviation---样本标准差sample coefficient of variation---样本变异系数standardized sample random variable---标准化样本随机变量sample skewness coefficient---样本偏度系数sample kurtosis coefficient---样本峰度系数sample covariance---样本协方差sample correlation coefficient---样本相关系数standard error---标准误差interval estimator---区间估计statistical tolerance interval---统计容忍区间statistical tolerance limit---统计容忍限confidence interval---置信区间one-sided confidence interval---单侧置信区间prediction interval---预测区间estimate---估计值error of estimation---估计误差bias---偏差unbiased estimator---无偏估计量maximum likelihood estimator---极大似然估计量estimation---估计maximum likelihood estimation---极大似然估计likelihood function---似然函数profile likelihood function---剖面函数hypothesis---假设null hypothesis---原假设alternative hypothesis---备择假设simple hypothesis---简单假设composite hypothesis---复合假设significance level---显著性水平type i error---第一类错误type ii error---第二类错误statistical test---统计检验significance test---显著性检验p-value---p值power of a test---检验功效power curve---功效曲线test statistic---检验统计量graphical descriptive statistics---图形描述性统计量numerical descriptive statistics---数值描述性统计量classes---类(组)class---类(组)class limits; class boundaries---组限mid-point of class---组中值class width---组距frequency---频数frequency distribution---频数分布histogram---直方图bar chart---条形图cumulative frequency---累积频数relative frequency---频率cumulative relative frequency---累积频率sample space---样本空间event---事件complementary event---对立事件independent events---独立事件probability [of an event A]---[事件A的]概率conditional probability---条件概率distribution function [of a random variable x]---[随机变量X的]分布函数family of distributions---分布族parameter---参数random variable---随机变量probability distribution---概率分布distribution---分布expectation---期望p-quantile---p分位数median---中位数quartile---四分位数one-dimensional probability distribution---一维概率分布one-dimensional distribution---一维分布multivariate probability distribution---多维概率分布multivariate distribution---多维分布marginal probability distribution---边缘概率分布marginal distribution---边缘分布conditional probability distribution---条件概率分布conditional distribution---条件分布regression curve---回归曲线regression surface---回归曲面discrete probability distribution---离散概率分布discrete distribution---离散分布continuous probability distribution---连续概率分布continuous distribution---连续分布probability [mass] function---概率函数mode of probability [mass] function---概率函数的众数probability density function---概率密度函数mode of probability density function---概率密度函数的众数discrete random variable---离散随机变量continuous random variable---连续随机变量centred probability distribution---中心化概率分布centred random variable---中心化随机变量standardized probability distribution---标准化概率分布standardized random variable---标准化随机变量moment of order r---r阶[原点]矩means---均值moment of order r = 1---一阶矩mean---均值variance---方差standard deviation---标准差coefficient of variation---变异系数coefficient of skewness---偏度系数coefficient of kurtosis---峰度系数joint moment of order r and s---(r,s)阶联合[原点]矩joint central moment of order r and s---(r,s)阶联合中心矩covariance---协方差correlation coefficient---相关系数multinomial distribution---多项分布binomial distribution---二项分布poisson distribution---泊松分布hypergeometric distribution---超几何分布negative binomial distribution---负二项分布normal distribution, gaussian distribution---正态分布standard normal distribution, standard gaussian distribution---标准正态分布lognormal distribution---对数正态分布t distribution, student's distribution---t分布degrees of freedom---自由度f distribution---f分布gamma distribution---伽玛分布，t分布chi-squared distribution---卡方分布，x²分布exponential distribution---指数分布beta distribution---贝塔分布，β分布uniform distribution, rectangular distribution---均匀分布type i value distribution, gumbel distribution---i型极值分布type ii value distribution, gumbel distribution---ii型极值分布weibull distribution---韦布尔分布type iii value distribution, gumbel distribution---iii型极值分布multivariate normal distribution---多维正态分布bivariate normal distribution---二维正态分布standard bivariate normal distribution---标准二维正态分布sampling distribution---抽样分布probability space---概率空间analysis of variance (anova)---方差分析covariance---协方差correlation coefficient---相关系数linear regression---线性回归multiple regression---多元回归logistic regression---逻辑回归principal component analysis (pca)---主成分分析cluster analysis---聚类分析factor analysis---因子分析bayesian statistics---贝叶斯统计time series analysis---时间序列分析non-parametric statistics---非参数统计survival analysis---生存分析data mining---数据挖掘machine learning---机器学习big data---大数据decision tree---决策树random forest---随机森林support vector machine (svm)---支持向量机neural network---神经网络deep learning---深度学习outlier detection---异常值检测cross validation---交叉验证moment---矩conditional probability---条件概率joint distribution---联合分布marginal distribution---边缘分布bayes' theorem---贝叶斯定理central limit theorem---中心极限定理law of large numbers---大数定律likelihood function---似然函数consistent estimator---一致性估计point estimation---点估计interval estimation---区间估计decision theory---决策理论bayesian estimation---贝叶斯估计sequential analysis---序列分析stochastic process---随机过程markov chain---马尔可夫链poisson process---泊松过程random sampling---随机抽样stratified sampling---分层抽样systematic sampling---系统抽样cluster sampling---簇抽样nonparametric test---非参数检验chi-square test---卡方检验t-test---t 检验f-test---f 检验。
统计词汇大全(中英文对照)
Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA （analysis of variance）, 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组分组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, 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。
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第一章数据分析：描述图形和从图形出収Association相关Bar chart条形图Bivariate data双变量Boxplot盒式图Center中心值包括mean平均值median中位数Conditional probability条件概率Continuous data连续数据，取自某个区间Continuous random variable连续随机变量Correlation coefficient相关系数，表征定量数据间的线性相关程度Correlation is not causation相关关系不意味着因果关系Discrete data离散数据Discrete random variable离散随机变量Dotplot点图First quartile第一四分位秩（下四分位秩）Five(5)-number summary5数值方法，即min value,Q1,median,Q3,max value Histogram直方图Inter quartile range四分位距Least-squares regression line最小二乘回归线Line of best fit最佳拟合线Marginal and joint frequencies（双向表中的）边际频数和联合频数Mean平均值Median中位数Mode众数分布中最常出现的值bell-shaped钟形数据分布形式Negatively associated 负相关Outlier极端值Percentile rank百分位秩Positively associated正相关Probability histogram概率直方图，横轴为变量大小，纵轴为概率Proportion比值Categorical data=Qualitative data定性数据用类别描述的数据Numerical data=Quantitative data定量数据用数字描述的数据Quartiles四分位秩，即数据系列的25%、50%、75%位置的数据Range全距，最大值与最小值之差Residual残差Resistant statistic抗性统计量Scatterplot散点图Skewed歪斜的非对称分布，如Skewed to the right尾部向右延伸Spread离差全部数据的分布范围和离散程度，如全距、四分位距、标准差Standard deviation标准差Stemplot(stem-and-leaf plot)茎叶图Symmetric对称的Third quartile第三四分位秩（上四分位秩）Two-way table双向表，以列表方式表示两个（多个）变量同时出现的频数Uniform均匀分布Univariate data单变量数据Variance方差z scorez计分某个数值到平均值距离与标准差比值第二章——抽样和实验：研究的规划与实施Block区组（模块）Block design区组（模块化）设计Census普查Completely randomized design 完全随机化设计Confounding variable混淆变量Double blind双盲Experimental units实验单位Explanatory variable说明变量Matched pairs成对Matched pairs design成对设计Nonresponse bias无回应偏差，调查对象无法或拒绝参加Observational study观察研究Observed values观测值Outcome结果Parameter（总体的）参数Placebo安慰剂通常对对比组所做处理Placebo effect安慰剂效应，通常为正面作用所形成的正向心理暗示Probability sample概率抽样，一种随机抽样技术Randomization随机化Random sample随机抽样Replication试验的重复性Representative sample代表性样本，具有总体的本质属性的样本Response bias回应偏差，调查中，问题不客观或带有指向性，或者问题涉及到隐私，造成的称为回应偏差Response variable反应变量Sample space样本空间，试验中所有可能的互斥结果Sample survey抽样调查Simple random sample简单随机抽样Statistic（样本的）统计量Stratified random sample 分层随机抽样Subjects试验对象，特指人Survey调查Treatment variable处理变量Undercoverage bias覆盖面偏差Voluntary response bias志愿者偏差第三章——预测模式：用概率和模拟来分析随机现象Binomial distribution二项分布Central limit theorem中心极限定理Complement of an event 互补事件Degrees of freedom自由度Density function密度函数Empirical Rule经验法则正态分布中，68%，95%，99.7%对应的z-计分Estimate估计Event事件Expected value期望值Geometric distribution 几何分布Independent events相互独立事件Independent variable独立变量Mutually exclusive events 互斥事件Normal curve正态曲线Normal distribution正态分布Probability distribution 概率分布Random phenomenon随机现象Random variable随机变量Sampling distribution of a statistic 统计量（比例或者均值）的抽样分布Standard normal distribution标准正态分布t distributiont分布t statistict统计量第四章——统计推断：参数估计和假设检验Alternative hypothesis备择假设另：null hypothesis零假设Chi-square卡方分布Goodness-of-Fit Test拟合度检验Confidence interval置信区间Confidence level置信水平Critical value临界值Homogeneity of proportions 比例的齐次性Inferential statistics推断统计Margin of error误差范围One-sided alternative单侧对立假设One-sided test单侧检验p-values P值Point estimate点估计Power of the test推翻错误零假设的概率Robust强烈的同strongSignificance level显著性水平Standard error标准误差统计推断可靠性指标之一Statistically significant 统计上显著的Test statistic检验统计量或者卡方，都有公式）Two-sided alternative 双侧备择Two-sided test双向检验Type-I error一类错误/α错误Type-II error二类错误/β错误Unbiased estimate无偏估计descriptive statistics是描述性统计，就是描述样本的统计特征，并不深入了解其内部规律，比如求平均数。
inferential statistics是推论统计，研究如何根据样本数据去推断总体数量特征的方法。
它是在对样本数据进行描述的基础上，对统计总体的未知数量特征做出以概率形式表述的推断。
更概括地说，是在一段有限的时间内，通过对一个随机过程的观察来进行推断的标准差，variance平方差。