统计学专业英语教程

✍ Scribed by 王忠玉，宋要武

• Publisher: 电子工业出版社
• Year: 2016
• Tongue: Chinese
• Leaves: 395
• Category: Library

✦ Synopsis

本书内容分为三部分：第一部分是描述统计学，共有 7 个单元，包括统计学初步、单变量数据的描
述分析、两个变量数据的描述分析、概率初步、离散概率模型、连续概率模型、抽样分布和中心极限定
理；第二部分是推断统计学（数理统计学），共有 4 个单元，包括统计推导初步、一个总体的统计推断、
两个总体的统计推断、简单回归、回归的统计分析；第三部分是统计学与数据科学专题，只有 1 个单元。
和同类书籍相比，本书具有如下特点：（1）比较系统地阐述基础统计学的知识，即以初阶统计学的
基本内容为主体，又适当地加入并介绍中阶统计学的部分内容；（2）在大多数章后，我们提供课外进一
步阅读和学习的补充知识，有统计学家简介等；（3）紧跟当今时代发展，给出“统计学与数据科学”的
阅读学习内容。另外，在每一章前面，我们精心选取了一些著名统计学家或教授的名言或警句，同时，
特别绘制了有趣的漫画。本书提供部分习题参考答案、教学 PPT、音频资料、部分课文参考译文及其他
辅助资料，读者可从华信教育资源网 www.hxedu.com.cn 免费下载，也可扫描二维码获取。
本书适合于各个专业对统计学专业英语感兴趣的大学生，学习双语统计学的统计学专业大学生，希
望学习和掌握中阶统计学的相关专业的低年级研究生，以及有关的科研人员等。

✦ Table of Contents

扉页
内 容 简 介
序 言
目 录
Part I Descriptive Statistics
Unit 1 Statistics
1.1 What is Statistics?
1.1.1 Meanings of Statistics
1.1.2 Definition of Statistics
1.1.3 Types of Statistics
1.1.4 Applications of Statistics
1.2 The language of Statistics
1.2.1 Population and Sample
1.2.2 Kinds of Variables
1.3 Measurability and Variability
1.4 Data Collection
1.4.1 The Data Collection Process
1.4.2 Sampling Frame and Elements
1.5 Single-Stage Methods
1.5.1 Simple Random Sample
1.5.2 Systematic Sample
1.6 Multistage Methods
1.7 Types of Statistical Study
1.8 The Process of a Statistical Study
Glossary
Reading English Materials
Passage 1. What is Statistics?
Passage 2. From Data to Foresight
Problems
Unit 2 Descriptive Analysis of Single-Variable Data
2.1 Graphs, Pareto Diagrams, and Stem-and-Leaf Displays
2.1.1 Qualitative Data
2.1.2 Quantitative Data
2.2 Frequency Distributions and Histograms
2.2.1 Frequency Distribution
2.2.2 Histograms
2.2.3 Cumulative Frequency Distribution and Ogives
2.3 Measures of Central Tendency
2.3.1 Finding the Mean
2.3.2 Finding the Median
2.3.3 Finding the Mode
2.3.4 Finding the Midrange
2.4 Measures of Dispersion
2.4.1 Sample Standard Deviation
2.5 Measures of Position
2.5.1 Quartiles
2.5.2 Percentiles
2.5.3 Other Measures of Position
2.6 Interpreting and Understanding Standard Deviation
2.6.1 The Empirical Rule and Testing for Normality
2.6.2 Chebyshev’s Theorem
Glossary
Problems
Unit 3 Descriptive Analysis of Bivariate Data
3.1 Bivariate Data
3.1.1 Two Qualitative Variables
3.1.2 One Qualitative and One Quantitative Variable
3.1.3 Two Quantitative Variables
3.2 Linear Correlation
3.2.1 Calculating the Linear Correlation Coefficient, r
3.2.2 Causation and Lurking Variables
3.3 Linear Regression
3.3.1 Line of Best Fit
3.3.2 Making Predictions
Reading English Materials
Passage 1. The First Regression
Passage 2. Simpson’s Paradox
Problems
Unit 4 Introduction to Probability
4.1 Sample Spaces, Events and Sets
4.1.1 Introduction
4.1.2 Sample Spaces
4.1.3 Events
4.1.4 Set Theory
4.2 Probability Axioms and Simple Counting Problems
4.2.1 Probability Axioms and Simple Properties
4.2.2 Interpretations of Probability
4.2.3 Classical Probability
4.2.4 The Multiplication Principle
4.3 Permutations and Combinations
4.3.1 Introduction
4.3.2 Permutations
4.3.3 Combinations
4.3.4 The Difference Between Permutations and Combinations
4.4 Conditional Probability and the Multiplication Rule
4.4.1 Conditional Probability
4.4.2 The Multiplication Rule
4.5 Independent Events, Partitions and Bayes Theorem
4.5.1 Independence
4.5.2 Partitions
4.5.3 Law of Total Probability
4.5.4 Bayes Theorem
4.5.5 Bayes Theorem for Partitions
Reading English Materials
Passage 1. Probability and Odds
Passage 2. The Relationship between Odds and Probability
Passage 3. How the Odds Change across the Range of the Probability
Problems
Unit 5 Discrete Probability Models
5.1 Introduction, Mass Functions and Distribution Functions
5.1.1 Introduction
5.1.2 Probability Mass Functions (PMFs)
5.1.3 Cumulative Distribution Functions (CDFs)
5.2 Expectation and Variance for Discrete Random Quantities
5.2.1 Expectation
5.2.2 Variance
5.3 Properties of Expectation and Variance
5.3.1 Expectation of a Function of a Random Quantity
5.3.2 Expectation of a Linear Transformation
5.3.3 Expectation of the Sum of Two Random Quantities
5.3.4 Expectation of an Independent Product
5.3.5 Variance of an Independent Sum
5.4 The Binomial Distribution
5.4.1 Introduction
5.4.2 Bernoulli Random Quantities
5.4.3 The Binomial Distribution
5.4.4 Expectation and Variance of a Binomial Random Quantity
5.5 The Geometric Distribution
5.5.1 PMF
5.5.2 CDF
5.5.3 Useful Series in Probability
5.5.4 Expectation and Variance of Geometric Random Quantities
5.6 The Poisson Distribution
5.6.1 Poisson as the Limit of a Binomial
5.6.2 PMF
5.6.3 Expectation and Variance of Poisson
5.6.4 Sum of Poisson Random Quantities
5.6.5 The Poisson Process
Reading English Materials
Passage 1. The Founder of Modern Statistics—Karl Pearson
Passage 2. The Relations of Several Discrete Probability Models
Problems
Unit 6 Discrete Probability Models
6.1 Introduction, PDF and CDF
6.1.1 Introduction
6.1.2 The Probability Density Function
6.1.3 The Distribution Function
6.1.4 Median and Quartiles
6.2 Properties of Continuous Random Quantities
6.2.1 Expectation and variance of continuous random quantities
6.2.2 PDF and CDF of a Linear Transformation
6.3 The Uniform Distribution
6.4 The Exponential Distribution
6.4.1 Definition and Properties
6.4.2 Relationship with the Poisson Process
6.4.3 The Memoryless Property
6.5 The Normal Distribution
6.5.1 Definition
6.5.2 Properties
6.6 The Standard Normal Distribution
6.6.1 Properties of the Standard Normal Distribution
6.6.2 Finding Area to The Right of z = 0
6.6.3 Finding Area in The Right Tail of a Normal Curve
6.6.4 Finding Area to the Left of a Positive z Value
6.6.5 Finding Area from a Negative z to z = 0
6.6.6 Finding Area in the Left Tail of a Normal Curve
6.6.7 Finding Area from A Negative z to a Positive z
6.6.8 Finding Area Between two z Values of the Same Sign
6.6.9 Finding z-Scores Associated with a Percentile
6.6.10 Finding z-scores that Bound an Area
6.7 Applications of Normal Distributions
6.7.1 Probabilities and Normal Curves
6.7.2 Using the Normal Curve and z
6.8 Specific z-score
6.8.1 Visual Interpretation of z(a)
6.8.2 Determining Corresponding z Values for z (a)
6.8.3 Determining z-scores for Bounded Areas
6.9 Normal Approximation of Binomial and Poisson
6.9.1 Normal Approximation of the Binomial
6.9.2 Normal Approximation of the Poisson
Problems
Unit 7 Sampling Distributions and CLT
7.1 Sampling Distributions
7.1.1 Forming a Sampling Distribution of Means
7.1.2 Creating a Sampling Distribution of Sample Means
7.2 The Sampling Distribution of Sample Means
7.2.1 Central Limit Theorem
7.2.2 Constructing a Sampling Distribution of Sample Means
7.3 Application of the Sampling Distribution of Sample Means
7.3.1 Converting Information into z-scores
7.3.2 Distribution of and Increasing Individual Sample Size
7.4 Advanced Central Limit Theorem
7.4.1 Central Limit Theorem (Sample Mean)
7.4.2 Central Limit Theorem (Sample Sum)
Problems
Part II Inferential Statistics
Unit 8 Introduction to Statistical Inferences
8.1 Point Estimation and Interval Estimation
8.1.1 Point Estimate
8.1.2 Interval Estimate
8.2 Estimation of Mean m (s Known)
8.2.1 The Principle of Constructing a Confidence Interval
8.2.2 Applications
8.2.3 Sample Size and Confidence Interval
8.3 Introduction to Hypothesis Testing
8.3.1 Null Hypothesis and Alternative Hypothesis
8.3.2 Four Possible Outcomes in a Hypothesis Test
8.4 Formulating the Statistical Null and Alternative Hypotheses
8.4.1 Writing Null and Alternative Hypothesis in One-Tailed Situation
8.4.2 Writing Null and Alternative Hypothesis in Two-Tailed Situation
8.5 Hypothesis Test of Mean m (s Known): A Probability-Value Approach
8.5.1 One-Tailed Hypothesis Test Using the p-Value Approach
8.5.2 Two-Tailed Hypothesis Test Using the p-Value Approach
8.5.3 Evaluating the p-Value Approach
8.6 Hypothesis Test of Mean m (s Known): A Classical Approach
8.6.1 One-Tailed Hypothesis Test Using the Classical Approach
8.6.2 Two-Tailed Hypothesis Test Using the Classical Approach
Problems
Unit 9 Inferences Involving One Population
9.1 Inferences about the Mean m (s Unknown)
9.1.1 Using the t-Distribution Table
9.1.2 Confidence Interval Procedure
9.1.3 Hypothesis-Testing Procedure
9.2 Inferences about the Binomial Probability of Success
9.2.1 Confidence Interval Procedure
9.2.2 Determining Sample Size
9.2.3 Hypothesis-Testing Procedure
9.3 Inferences about the Variance and Standard Deviation
9.3.1 Critical Values of Chi-Square
9.3.2 Hypothesis-Testing Procedure
Problems
Unit 10 Inferences Involving Two Populations
10.1 Dependent and Independent Samples
10.2 Inferences Concerning the Mean Difference Using Two Dependent Samples
10.2.1 Procedures and Assumptions for Inferences Involving Paired Data
10.2.2 Confidence Interval Procedure
10.2.3 Hypothesis-Testing Procedure
10.3 Inferences Concerning the Difference between Means Using Two Independent
Samples
10.3.1 Confidence Interval Procedure
10.3.2 Hypothesis-Testing Procedure
10.4 Inferences Concerning the Difference between Proportions
10.4.1 Confidence Interval Procedure
10.4.2 Hypothesis-Testing Procedure
10.5 Inferences Concerning the Ratio of Variances Using Two Independent Samples
10.5.1 Writing for the Equality of Variances
10.5.2 Using the F-Distribution
10.5.3 One-Tailed Hypothesis Test for the Equality of Variances
10.5.4 Critical F-Values for One- and Two-Tailed Tests
Problems
Unit 11 An Introduction to Simple Regression
11.1 Regression as a Best Fitting Line
11.1.1 Regression as a Best Fitting Line
11.1.2 Errors and Residuals
11.2 Interpreting OLS Estimates
11.3 Fitted Values and R2: Measuring the Fit of a Regression Model
11.4 Nonlinearity in Regression
Reading English Materials
Problems
Part III Statistical Methods and Data Science
Unit 12 Statistics and Data Science
12.1 Statistics and Data Science (I)
12.1.1 What is Data Science
12.1.2 Statistics and Data Science
12.2 Statistics and Data Science (II)
12.2.1 Statistics as Part of Data Science
12.2.2 The Modern Statistical Analysis Process
12.2.3 Statistician and Data Scientist
12.3 Statistical Thinking
12.3.1 What is Statistical Thinking
12.3.2 The Two Cultures of Statistical Modeling
12.3.3 A New Research Community
12.4 Distinguishing Analytics, Business Intelligence, Data Science
12.4.1 Analytics
12.4.2 Business Intelligence
12.4.3 Data Science
Reading English Materials
Problems
Commonly Used Statistical Terms
Appendix A Commonly Used Statistical Tables
Appendix B Summary of Univariate Descriptive Statistics and Graphs for the Four Level of Measurement
Appendix C Order of Magnitude of Data
References