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统计学专业英语教程
作者：王忠玉，宋要武 著
出版社：电子工业出版社
出版时间：2016-08-01
ISBN：9787121289286
定价：¥49.80
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内容简介
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。
作者简介
王忠玉，1963年9月生，教授，哈尔滨工业大学管理学博士，哈尔滨工业大学应用数学硕士，现为吉林大学数量经济学博士后。从事现代经济计量学、应用统计学、金融经济学等研究在《管理世界》、《经济评论》、《中国管理科学》、《国际金融研究》、《中国统计》、《统计教育》等管理类及经济学刊物上发表论文20余篇，出版译著作2本、主审1本，主持并完成省级科研项目1项。现为黑龙江省数量与技术经济学会副秘书长，并且是《金融学前沿译丛》编委会员员、《当代世界学术名著？经济学系列》编委会委员、中国运筹学会会员。
目录
Part I Descriptive Statistics
Unit 1 Statistics 3
1．1 What is Statistics? 4
1．1．1 Meanings of Statistics 4
1．1．2 Definition of Statistics 5
1．1．3 Types of Statistics 6
1．1．4 Applications of Statistics 6
1．2 The language of Statistics 9
1．2．1 Population and Sample 9
1．2．2 Kinds of Variables 11
1．3 Measurability and Variability 14
1．4 Data Collection 16
1．4．1 The Data Collection Process 17
1．4．2 Sampling Frame and Elements 18
1．5* Single-Stage Methods 21
1．5．1 Simple Random Sample 21
1．5．2 Systematic Sample 22
1．6* Multistage Methods 25
1．7* Types of Statistical Study 27
1．8 The Process of a Statistical Study 31
Glossary 34
Reading English Materials 35
Passage 1． What is Statistics? 35
Passage 2． From Data to Foresight 35
Problems 36
Unit 2 Descriptive Analysis of Single-Variable Data 40
2．1 Graphs， Pareto Diagrams， and Stem-and-Leaf Displays 41
2．1．1 Qualitative Data 41
2．1．2 Quantitative Data 43
2．2 Frequency Distributions and Histograms 47
2．2．1 Frequency Distribution 47
2．2．2 Histograms 51
2．2．3 Cumulative Frequency Distribution and Ogives 53
2．3 Measures of Central Tendency 55
2．3．1 Finding the Mean 55
2．3．2 Finding the Median 56
2．3．3 Finding the Mode 57
2．3．4 Finding the Midrange 58
2．4 Measures of Dispersion 60
2．4．1 Sample Standard Deviation 62
2．5 Measures of Position 64
2．5．1 Quartiles 64
2．5．2 Percentiles 64
2．5．3 Other Measures of Position 66
2．6 Interpreting and Understanding Standard Deviation 70
2．6．1 The Empirical Rule and Testing for Normality 70
2．6．2 Chebyshev’s Theorem 72
Glossary 74
Problems 75
Unit 3 Descriptive Analysis of Bivariate Data 79
3．1 Bivariate Data 80
3．1．1 Two Qualitative Variables 80
3．1．2 One Qualitative and One Quantitative Variable 82
3．1．3 Two Quantitative Variables 83
3．2 Linear Correlation 85
3．2．1 Calculating the Linear Correlation Coefficient， r 86
*3．2．2 Causation and Lurking Variables 89
3．3 Linear Regression 91
3．3．1 Line of Best Fit 92
3．3．2 Making Predictions 97
Reading English Materials 99
Passage 1． The First Regression 99
Passage 2． Simpson’s Paradox 99
Problems 100
Unit 4 Introduction to Probability 104
4．1 Sample Spaces， Events and Sets 105
4．1．1 Introduction 105
4．1．2 Sample Spaces 105
4．1．3 Events 106
4．1．4 Set Theory 108
4．2 Probability Axioms and Simple Counting Problems 109
4．2．1 Probability Axioms and Simple Properties 109
4．2．2 Interpretations of Probability 111
4．2．3 Classical Probability 112
4．2．4 The Multiplication Principle 113
4．3 Permutations and Combinations 115
4．3．1 Introduction 115
4．3．2 Permutations 116
4．3．3 Combinations 118
4．3．4 The Difference Between Permutations and Combinations 120
4．4 Conditional Probability and the Multiplication Rule 122
4．4．1 Conditional Probability 122
4．4．2 The Multiplication Rule 123
4．5 Independent Events， Partitions and Bayes Theorem 124
4．5．1 Independence 124
4．5．2 Partitions 125
4．5．3 Law of Total Probability 126
4．5．4 Bayes Theorem 126
4．5．5 Bayes Theorem for Partitions 127
Reading English Materials 130
Passage 1． Probability and Odds 130
Passage 2． The Relationship between Odds and Probability 130
Passage 3． How the Odds Change across the Range of the Probability 131
Problems 132
Unit 5 Discrete Probability Models 134
5．1 Introduction， Mass Functions and Distribution Functions 135
5．1．1 Introduction 135
5．1．2 Probability Mass Functions (PMFs) 136
5．1．3 Cumulative Distribution Functions (CDFs) 137
5．2 Expectation and Variance for Discrete Random Quantities 138
5．2．1 Expectation 138
5．2．2 Variance 139
5．3 Properties of Expectation and Variance 140
5．3．1 Expectation of a Function of a Random Quantity 140
5．3．2 Expectation of a Linear Transformation 140
5．3．3 Expectation of the Sum of Two Random Quantities 141
5．3．4 Expectation of an Independent Product 141
5．3．5 Variance of an Independent Sum 142
5．4 The Binomial Distribution 142
5．4．1 Introduction 142
5．4．2 Bernoulli Random Quantities 143
5．4．3 The Binomial Distribution 143
5．4．4 Expectation and Variance of a Binomial Random Quantity 145
5．5 The Geometric Distribution 146
5．5．1 PMF 146
5．5．2 CDF 147
5．5．3 Useful Series in Probability 148
5．5．4 Expectation and Variance of Geometric Random Quantities 148
5．6 The Poisson Distribution 149
5．6．1 Poisson as the Limit of a Binomial 149
5．6．2 PMF 150
5．6．3 Expectation and Variance of Poisson 151
5．6．4 Sum of Poisson Random Quantities 152
5．6．5 The Poisson Process 152
Reading English Materials 154
Passage 1． The Founder of Modern Statistics―Karl Pearson 154
Passage 2． The Relations of Several Discrete Probability Models 154
Problems 155
Unit 6 Discrete Probability Models 158
6．1 Introduction， PDF and CDF 159
6．1．1 Introduction 159
6．1．2 The Probability Density Function 159
6．1．3 The Distribution Function 160
6．1．4 Median and Quartiles 161
6．2 Properties of Continuous Random Quantities 161
6．2．1 Expectation and variance of continuous random quantities 161
6．2．2 PDF and CDF of a Linear Transformation 162
6．3 The Uniform Distribution 163
6．4 The Exponential Distribution 165
6．4．1 Definition and Properties 165
6．4．2 Relationship with the Poisson Process 166
6．4．3 The Memoryless Property 167
6．5 The Normal Distribution 168
6．5．1 Definition 168
6．5．2 Properties 168
6．6 The Standard Normal Distribution 169
6．6．1 Properties of the Standard Normal Distribution 170
6．6．2 Finding Area to The Right of z = 0 171
6．6．3 Finding Area in The Right Tail of a Normal Curve 171
6．6．4 Finding Area to the Left of a Positive z Value 172
6．6．5 Finding Area from a Negative z to z = 0 172
6．6．6 Finding Area in the Left Tail of a Normal Curve 172
6．6．7 Finding Area from A Negative z to a Positive z 172
6．6．8 Finding Area Between two z Values of the Same Sign 173
6．6．9 Finding z-Scores Associated with a Percentile 173
6．6．10 Finding z-scores that Bound an Area 174
6．7 Applications of Normal Distributions 175
6．7．1 Probabilities and Normal Curves 175
6．7．2 Using the Normal Curve and z 176
6．8 Specific z-score 178
6．8．1 Visual Interpretation of z(a) 179
6．8．2 Determining Corresponding z Values for z (a) 179
6．8．3 Determining z-scores for Bounded Areas 180
6．9 Normal Approximation of Binomial and Poisson 181
6．9．1 Normal Approximation of the Binomial 181
6．9．2 Normal Approximation of the Poisson 182
Problems 182
Unit 7 Sampling Distributions and CLT 187
7．1 Sampling Distributions 188
7．1．1 Forming a Sampling Distribution of Means 188
7．1．2 Creating a Sampling Distribution of Sample Means 189
7．2 The Sampling Distribution of Sample Means 192
7．2．1 Central Limit Theorem 193
7．2．2 Constructing a Sampling Distribution of Sample Means 194
7．3 Application of the Sampling Distribution of Sample Means 199
7．3．1 Converting Information into z-scores 199
7．3．2 Distribution of and Increasing Individual Sample Size 200
7．4 Advanced Central Limit Theorem 202
7．4．1 Central Limit Theorem (Sample Mean) 203
7．4．2 Central Limit Theorem (Sample Sum) 203
Problems 207
Part II Inferential Statistics
Unit 8 Introduction to Statistical Inferences 210
8．1 Point Estimation and Interval Estimation 211
8．1．1 Point Estimate 211
8．1．2 Interval Estimate 212
8．2 Estimation of Mean m (s Known) 214
8．2．1 The Principle of Constructing a Confidence Interval 214
8．2．2 Applications 216
8．2．3 Sample Size and Confidence Interval 217
8．3 Introduction to Hypothesis Testing 220
8．3．1 Null Hypothesis and Alternative Hypothesis 220
8．3．2 Four Possible Outcomes in a Hypothesis Test 222
8．4 Formulating the Statistical Null and Alternative Hypotheses 226
8．4．1 Writing Null and Alternative Hypothesis in One-Tailed Situation 226
8．4．2 Writing Null and Alternative Hypothesis in Two-Tailed Situation 227
8．5 Hypothesis Test of Mean m (s Known)： A Probability-Value Approach 228
8．5．1 One-Tailed Hypothesis Test Using the p-Value Approach 229
8．5．2 Two-Tailed Hypothesis Test Using the p-Value Approach 233
8．5．3 Evaluating the p-Value Approach 234
8．6 Hypothesis Test of Mean m (s Known)： A Classical Approach 235
8．6．1 One-Tailed Hypothesis Test Using the Classical Approach 236
8．6．2 Two-Tailed Hypothesis Test Using the Classical Approach 239
Problems 241
Unit 9 Inferences Involving One Population 246
9．1 Inferences about the Mean m (s Unknown) 247
9．1．1 Using the t-Distribution Table 249
9．1．2 Confidence Interval Procedure 251
9．1．3 Hypothesis-Testing Procedure 252
9．2 Inferences about the Binomial Probability of Success 258
9．2．1 Confidence Interval Procedure 259
9．2．2 Determining Sample Size 261
9．2．3 Hypothesis-Testing Procedure 263
9．3 Inferences about the Variance and Standard Deviation 268
9．3．1 Critical Values of Chi-Square 269
9．3．2 Hypothesis-Testing Procedure 270
Problems 279
Unit 10 Inferences Involving Two Populations 284
10．1 Dependent and Independent Samples 285
10．2 Inferences Concerning the Mean Difference Using Two Dependent Samples 287
10．2．1 Procedures and Assumptions for Inferences Involving Paired Data 287
10．2．2 Confidence Interval Procedure 288
10．2．3 Hypothesis-Testing Procedure 290
10．3 Inferences Concerning the Difference between Means Using Two Independent
Samples 294
10．3．1 Confidence Interval Procedure 295
10．3．2 Hypothesis-Testing Procedure 297
10．4 Inferences Concerning the Difference between Proportions 301
10．4．1 Confidence Interval Procedure 303
10．4．2 Hypothesis-Testing Procedure 304
10．5 Inferences Concerning the Ratio of Variances Using Two Independent Samples 308
10．5．1 Writing for the Equality of Variances 308
10．5．2 Using the F-Distribution 309
10．5．3 One-Tailed Hypothesis Test for the Equality of Variances 310
10．5．4 Critical F-Values for One- and Two-Tailed Tests 313
Problems 315
Unit 11 An Introduction to Simple Regression 321
11．1 Regression as a Best Fitting Line 322
11．1．1 Regression as a Best Fitting Line 322
11．1．2 Errors and Residuals 324
11．2 Interpreting OLS Estimates 326
11．3 Fitted Values and R2： Measuring the Fit of a Regression Model 328
11．4 Nonlinearity in Regression 331
Reading English Materials 335
Problems 336
Part III Statistical Methods and Data Science
Unit 12 Statistics and Data Science 339
12．1 Statistics and Data Science (I) 340
12．1．1 What is Data Science 340
12．1．2 Statistics and Data Science 340
12．2 Statistics and Data Science (II) 343
12．2．1 Statistics as Part of Data Science 343
12．2．2 The Modern Statistical Analysis Process 344
12．2．3 Statistician and Data Scientist 345
12．3 Statistical Thinking 348
12．3．1 What is Statistical Thinking 348
12．3．2 The Two Cultures of Statistical Modeling 348
12．3．3 A New Research Community 350
12．4 Distinguishing Analytics， Business Intelligence， Data Science 352
12．4．1 Analytics 352
12．4．2 Business Intelligence 355
12．4．3 Data Science 356
Reading English Materials 359
Problems 361
Commonly Used Statistical Terms 362
Appendix A Commonly Used Statistical Tables 367
Appendix B Summary of Univariate Descriptive Statistics and Graphs for the Four
Level of Measurement 379
Appendix C Order of Magnitude of Data 380
References 381
Unit 1 Statistics 3
1．1 What is Statistics? 4
1．1．1 Meanings of Statistics 4
1．1．2 Definition of Statistics 5
1．1．3 Types of Statistics 6
1．1．4 Applications of Statistics 6
1．2 The language of Statistics 9
1．2．1 Population and Sample 9
1．2．2 Kinds of Variables 11
1．3 Measurability and Variability 14
1．4 Data Collection 16
1．4．1 The Data Collection Process 17
1．4．2 Sampling Frame and Elements 18
1．5* Single-Stage Methods 21
1．5．1 Simple Random Sample 21
1．5．2 Systematic Sample 22
1．6* Multistage Methods 25
1．7* Types of Statistical Study 27
1．8 The Process of a Statistical Study 31
Glossary 34
Reading English Materials 35
Passage 1． What is Statistics? 35
Passage 2． From Data to Foresight 35
Problems 36
Unit 2 Descriptive Analysis of Single-Variable Data 40
2．1 Graphs， Pareto Diagrams， and Stem-and-Leaf Displays 41
2．1．1 Qualitative Data 41
2．1．2 Quantitative Data 43
2．2 Frequency Distributions and Histograms 47
2．2．1 Frequency Distribution 47
2．2．2 Histograms 51
2．2．3 Cumulative Frequency Distribution and Ogives 53
2．3 Measures of Central Tendency 55
2．3．1 Finding the Mean 55
2．3．2 Finding the Median 56
2．3．3 Finding the Mode 57
2．3．4 Finding the Midrange 58
2．4 Measures of Dispersion 60
2．4．1 Sample Standard Deviation 62
2．5 Measures of Position 64
2．5．1 Quartiles 64
2．5．2 Percentiles 64
2．5．3 Other Measures of Position 66
2．6 Interpreting and Understanding Standard Deviation 70
2．6．1 The Empirical Rule and Testing for Normality 70
2．6．2 Chebyshev’s Theorem 72
Glossary 74
Problems 75
Unit 3 Descriptive Analysis of Bivariate Data 79
3．1 Bivariate Data 80
3．1．1 Two Qualitative Variables 80
3．1．2 One Qualitative and One Quantitative Variable 82
3．1．3 Two Quantitative Variables 83
3．2 Linear Correlation 85
3．2．1 Calculating the Linear Correlation Coefficient， r 86
*3．2．2 Causation and Lurking Variables 89
3．3 Linear Regression 91
3．3．1 Line of Best Fit 92
3．3．2 Making Predictions 97
Reading English Materials 99
Passage 1． The First Regression 99
Passage 2． Simpson’s Paradox 99
Problems 100
Unit 4 Introduction to Probability 104
4．1 Sample Spaces， Events and Sets 105
4．1．1 Introduction 105
4．1．2 Sample Spaces 105
4．1．3 Events 106
4．1．4 Set Theory 108
4．2 Probability Axioms and Simple Counting Problems 109
4．2．1 Probability Axioms and Simple Properties 109
4．2．2 Interpretations of Probability 111
4．2．3 Classical Probability 112
4．2．4 The Multiplication Principle 113
4．3 Permutations and Combinations 115
4．3．1 Introduction 115
4．3．2 Permutations 116
4．3．3 Combinations 118
4．3．4 The Difference Between Permutations and Combinations 120
4．4 Conditional Probability and the Multiplication Rule 122
4．4．1 Conditional Probability 122
4．4．2 The Multiplication Rule 123
4．5 Independent Events， Partitions and Bayes Theorem 124
4．5．1 Independence 124
4．5．2 Partitions 125
4．5．3 Law of Total Probability 126
4．5．4 Bayes Theorem 126
4．5．5 Bayes Theorem for Partitions 127
Reading English Materials 130
Passage 1． Probability and Odds 130
Passage 2． The Relationship between Odds and Probability 130
Passage 3． How the Odds Change across the Range of the Probability 131
Problems 132
Unit 5 Discrete Probability Models 134
5．1 Introduction， Mass Functions and Distribution Functions 135
5．1．1 Introduction 135
5．1．2 Probability Mass Functions (PMFs) 136
5．1．3 Cumulative Distribution Functions (CDFs) 137
5．2 Expectation and Variance for Discrete Random Quantities 138
5．2．1 Expectation 138
5．2．2 Variance 139
5．3 Properties of Expectation and Variance 140
5．3．1 Expectation of a Function of a Random Quantity 140
5．3．2 Expectation of a Linear Transformation 140
5．3．3 Expectation of the Sum of Two Random Quantities 141
5．3．4 Expectation of an Independent Product 141
5．3．5 Variance of an Independent Sum 142
5．4 The Binomial Distribution 142
5．4．1 Introduction 142
5．4．2 Bernoulli Random Quantities 143
5．4．3 The Binomial Distribution 143
5．4．4 Expectation and Variance of a Binomial Random Quantity 145
5．5 The Geometric Distribution 146
5．5．1 PMF 146
5．5．2 CDF 147
5．5．3 Useful Series in Probability 148
5．5．4 Expectation and Variance of Geometric Random Quantities 148
5．6 The Poisson Distribution 149
5．6．1 Poisson as the Limit of a Binomial 149
5．6．2 PMF 150
5．6．3 Expectation and Variance of Poisson 151
5．6．4 Sum of Poisson Random Quantities 152
5．6．5 The Poisson Process 152
Reading English Materials 154
Passage 1． The Founder of Modern Statistics―Karl Pearson 154
Passage 2． The Relations of Several Discrete Probability Models 154
Problems 155
Unit 6 Discrete Probability Models 158
6．1 Introduction， PDF and CDF 159
6．1．1 Introduction 159
6．1．2 The Probability Density Function 159
6．1．3 The Distribution Function 160
6．1．4 Median and Quartiles 161
6．2 Properties of Continuous Random Quantities 161
6．2．1 Expectation and variance of continuous random quantities 161
6．2．2 PDF and CDF of a Linear Transformation 162
6．3 The Uniform Distribution 163
6．4 The Exponential Distribution 165
6．4．1 Definition and Properties 165
6．4．2 Relationship with the Poisson Process 166
6．4．3 The Memoryless Property 167
6．5 The Normal Distribution 168
6．5．1 Definition 168
6．5．2 Properties 168
6．6 The Standard Normal Distribution 169
6．6．1 Properties of the Standard Normal Distribution 170
6．6．2 Finding Area to The Right of z = 0 171
6．6．3 Finding Area in The Right Tail of a Normal Curve 171
6．6．4 Finding Area to the Left of a Positive z Value 172
6．6．5 Finding Area from a Negative z to z = 0 172
6．6．6 Finding Area in the Left Tail of a Normal Curve 172
6．6．7 Finding Area from A Negative z to a Positive z 172
6．6．8 Finding Area Between two z Values of the Same Sign 173
6．6．9 Finding z-Scores Associated with a Percentile 173
6．6．10 Finding z-scores that Bound an Area 174
6．7 Applications of Normal Distributions 175
6．7．1 Probabilities and Normal Curves 175
6．7．2 Using the Normal Curve and z 176
6．8 Specific z-score 178
6．8．1 Visual Interpretation of z(a) 179
6．8．2 Determining Corresponding z Values for z (a) 179
6．8．3 Determining z-scores for Bounded Areas 180
6．9 Normal Approximation of Binomial and Poisson 181
6．9．1 Normal Approximation of the Binomial 181
6．9．2 Normal Approximation of the Poisson 182
Problems 182
Unit 7 Sampling Distributions and CLT 187
7．1 Sampling Distributions 188
7．1．1 Forming a Sampling Distribution of Means 188
7．1．2 Creating a Sampling Distribution of Sample Means 189
7．2 The Sampling Distribution of Sample Means 192
7．2．1 Central Limit Theorem 193
7．2．2 Constructing a Sampling Distribution of Sample Means 194
7．3 Application of the Sampling Distribution of Sample Means 199
7．3．1 Converting Information into z-scores 199
7．3．2 Distribution of and Increasing Individual Sample Size 200
7．4 Advanced Central Limit Theorem 202
7．4．1 Central Limit Theorem (Sample Mean) 203
7．4．2 Central Limit Theorem (Sample Sum) 203
Problems 207
Part II Inferential Statistics
Unit 8 Introduction to Statistical Inferences 210
8．1 Point Estimation and Interval Estimation 211
8．1．1 Point Estimate 211
8．1．2 Interval Estimate 212
8．2 Estimation of Mean m (s Known) 214
8．2．1 The Principle of Constructing a Confidence Interval 214
8．2．2 Applications 216
8．2．3 Sample Size and Confidence Interval 217
8．3 Introduction to Hypothesis Testing 220
8．3．1 Null Hypothesis and Alternative Hypothesis 220
8．3．2 Four Possible Outcomes in a Hypothesis Test 222
8．4 Formulating the Statistical Null and Alternative Hypotheses 226
8．4．1 Writing Null and Alternative Hypothesis in One-Tailed Situation 226
8．4．2 Writing Null and Alternative Hypothesis in Two-Tailed Situation 227
8．5 Hypothesis Test of Mean m (s Known)： A Probability-Value Approach 228
8．5．1 One-Tailed Hypothesis Test Using the p-Value Approach 229
8．5．2 Two-Tailed Hypothesis Test Using the p-Value Approach 233
8．5．3 Evaluating the p-Value Approach 234
8．6 Hypothesis Test of Mean m (s Known)： A Classical Approach 235
8．6．1 One-Tailed Hypothesis Test Using the Classical Approach 236
8．6．2 Two-Tailed Hypothesis Test Using the Classical Approach 239
Problems 241
Unit 9 Inferences Involving One Population 246
9．1 Inferences about the Mean m (s Unknown) 247
9．1．1 Using the t-Distribution Table 249
9．1．2 Confidence Interval Procedure 251
9．1．3 Hypothesis-Testing Procedure 252
9．2 Inferences about the Binomial Probability of Success 258
9．2．1 Confidence Interval Procedure 259
9．2．2 Determining Sample Size 261
9．2．3 Hypothesis-Testing Procedure 263
9．3 Inferences about the Variance and Standard Deviation 268
9．3．1 Critical Values of Chi-Square 269
9．3．2 Hypothesis-Testing Procedure 270
Problems 279
Unit 10 Inferences Involving Two Populations 284
10．1 Dependent and Independent Samples 285
10．2 Inferences Concerning the Mean Difference Using Two Dependent Samples 287
10．2．1 Procedures and Assumptions for Inferences Involving Paired Data 287
10．2．2 Confidence Interval Procedure 288
10．2．3 Hypothesis-Testing Procedure 290
10．3 Inferences Concerning the Difference between Means Using Two Independent
Samples 294
10．3．1 Confidence Interval Procedure 295
10．3．2 Hypothesis-Testing Procedure 297
10．4 Inferences Concerning the Difference between Proportions 301
10．4．1 Confidence Interval Procedure 303
10．4．2 Hypothesis-Testing Procedure 304
10．5 Inferences Concerning the Ratio of Variances Using Two Independent Samples 308
10．5．1 Writing for the Equality of Variances 308
10．5．2 Using the F-Distribution 309
10．5．3 One-Tailed Hypothesis Test for the Equality of Variances 310
10．5．4 Critical F-Values for One- and Two-Tailed Tests 313
Problems 315
Unit 11 An Introduction to Simple Regression 321
11．1 Regression as a Best Fitting Line 322
11．1．1 Regression as a Best Fitting Line 322
11．1．2 Errors and Residuals 324
11．2 Interpreting OLS Estimates 326
11．3 Fitted Values and R2： Measuring the Fit of a Regression Model 328
11．4 Nonlinearity in Regression 331
Reading English Materials 335
Problems 336
Part III Statistical Methods and Data Science
Unit 12 Statistics and Data Science 339
12．1 Statistics and Data Science (I) 340
12．1．1 What is Data Science 340
12．1．2 Statistics and Data Science 340
12．2 Statistics and Data Science (II) 343
12．2．1 Statistics as Part of Data Science 343
12．2．2 The Modern Statistical Analysis Process 344
12．2．3 Statistician and Data Scientist 345
12．3 Statistical Thinking 348
12．3．1 What is Statistical Thinking 348
12．3．2 The Two Cultures of Statistical Modeling 348
12．3．3 A New Research Community 350
12．4 Distinguishing Analytics， Business Intelligence， Data Science 352
12．4．1 Analytics 352
12．4．2 Business Intelligence 355
12．4．3 Data Science 356
Reading English Materials 359
Problems 361
Commonly Used Statistical Terms 362
Appendix A Commonly Used Statistical Tables 367
Appendix B Summary of Univariate Descriptive Statistics and Graphs for the Four
Level of Measurement 379
Appendix C Order of Magnitude of Data 380
References 381
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