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《統計模型:理論和實踐(英文版·第2版)》內容簡介：Some books are correct. Some are clear. Some are useful. Some are entertaining. Few are even two of these. This book is all four. Statistical Models: Theory and Practice is lucid, candid and insightful, a joy to read. We are fortunate that David Freedman finished this new edition before his death in late 2008. We are deeply saddened by his passing, and we greatly admire the energy and cheer he brought to this volume——and many other projects——-during his final months.
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作者：（美國）弗里德曼（David A.Freedman）
David A.Freedman（1938-2008）是加州大學伯克利分校的統計學教授、傑出的數理統計學家，其研究范圍包括鞅不等式分析、Markov過程、抽樣、自助法等。他是美國科學學院（American Academy of Art and Sciences）院士。在2003年，美國科學院授予他John J.Carry科學進步獎，以表彰他對統計理論和實。
David A.Freedman（1938-2008）是加州大學伯克利分校的統計學教授、傑出的數理統計學家，其研究范圍包括鞅不等式分析、Markov過程、抽樣、自助法等。他是美國科學學院（American Academy of Art and Sciences）院士。在2003年，美國科學院授予他John J.Carry科學進步獎，以表彰他對統計理論和實。
目次
Table of Contents
Foreword to the Revised Edition iii
Preface v
1 Observational Studies and Experiments
1.1 Introduction 1
1.2 The HIP trial 4
1.3 Snow on cholera 6
1.4 Yule on the causes of poverty 9
Exercise set A 13
1.5 End notes 14
2 The Regression Line
2.1 Introduction 18
2.2 The regression line 18
2.3 Hookes law 22
Exercise set A 23
2.4 Complexities 23
2.5 Simple vs multiple regression 26
Exercise set B 26
2.6 End notes 28
3 Matrix Algebra
3.1 Introduction 29
Exercise set A 30
3.2 Determinants and inverses 31
Exercise set B 33
3.3 Random vectors 35
Exercise set C 35
3.4 Positive definite matrices 36
Exercise set D 37
3.5 The normal distribution 38
Exercise set E 39
3.6 If you want a book on matrix algebra 40
4 Multiple Regression
4.1 Introduction 41
Exercise set A 44
4.2 Standard errors 45
Things we dont need 49
Exercise set B 49
4.3 Explained variance in multiple regression 51
Association or causation? 53
Exercise set C 53
4.4 What happens to OLS if the assumptions break down? 53
4.5 Discussion questions 53
4.6 End notes 59
5 Multiple Regression: Special Topics
5.1 Introduction 61
5.20LSisBLUE 61
Exercise set A 63
5.3 Generalized least squares 63
Exercise set B 65
5.4 Examples on GLS 65
Exercise set C 66
5.5 What happens to GLS if the assumptions break down? 68
5.6 Normal theory 68
Statistical significance 70
Exercise set D 71
5.7 The F-test 72
The F-test in applied work 73
Exercise set E 74
5.8 Data snooping 74
Exercise set F 76
5.9 Discussion questions 76
5.10 End notes 78
6 Path Models
6.1 Stratification 81
Exercise set A 86
6.2 Hookes law revisited 87
Exercise set B 88
6.3 Political repression during the McCarthy era 88
Exercise set C 90
TABLE OF CONTENTS
6.4 Inferring causation by regression 91
Exercise set D 93
6.5 Response schedules for path diagrams 94
Selection vs intervention 101
Structural equations and stable parameters 101
Ambiguity in notation 102
Exercise set E 102
6.6 Dummy variables 103
Types of variables 104
6.7 Discussion questions 105
6.8 End notes 112
7 Maximum Likelihood
7.1 Introduction 115
Exercise set A 119
7.2 Probit models 121
Why not regression? 123
The latent-variable formulation 123
Exercise set B 124
Identification vs estimation 125
What if the Ui are N(/z, tr2)? 126
Exercise set C 127
7.3 Logit models 128
Exercise set D 128
7.4 The effect of Catholic schools 130
Latent variables 132
Response schedules 133
The second equation 134
Mechanics: bivariate probit 136
Why a model rather than a cross-tab? 138
Interactions 138
More on table 3 in Evans and Schwab 139
More on the second equation 139
Exercise set E 140
7.5 Discussion questions 141
7.6 End notes 150
8 The Bootstrap
8.1 Introduction 155
Exercise set A 166
8.2 Bootstrapping a model for energy demand 167
Exercise set B 173
8.3 End notes 174
9 Simultaneous Equations
9.1 Introduction 176
Exercise set A 181
9.2 Instrumental variables 181
Exercise set B 184
9.3 Estimating the butter model 184
Exercise set C 185
9.4 What are the two stages? 186
Invariance assumptions 187
9.5 A social-science example: education and fertility 187
More on Rindfuss et al 191
9.6 Covariates 192
9.7 Linear probability models 193
The assumptions 194
The questions 195
Exercise set D 196
9.8 More on IVLS 197
Some technical issues 197
Exercise set E 198
Simulations to illustrate IVLS 199
9.9 Discussion questions 200
9.10 End notes 207
10 Issues in Statistical Modeling
10.1 Introduction 209
The bootstrap 211
The role of asymptotics 211
Philosophers stones 211
The modelers response 212
10.2 Critical literature 212
10.3 Response schedules 217
10.4 Evaluating the models in chapters 7-9 217
10.5 Summing up 218
References 219
Answers to Exercises 235
TABLE OF CONTENTS
The Computer Labs 294
Appendix: Sample MATLAB Code 310
Reprints
Gibson on McCarthy 315
Evans and Schwab on Catholic Schools 343
Rindfuss et al on Education and Fertility 377
Schneider et al on Social Capital 402
Index 431
Foreword to the Revised Edition iii
Preface v
1 Observational Studies and Experiments
1.1 Introduction 1
1.2 The HIP trial 4
1.3 Snow on cholera 6
1.4 Yule on the causes of poverty 9
Exercise set A 13
1.5 End notes 14
2 The Regression Line
2.1 Introduction 18
2.2 The regression line 18
2.3 Hookes law 22
Exercise set A 23
2.4 Complexities 23
2.5 Simple vs multiple regression 26
Exercise set B 26
2.6 End notes 28
3 Matrix Algebra
3.1 Introduction 29
Exercise set A 30
3.2 Determinants and inverses 31
Exercise set B 33
3.3 Random vectors 35
Exercise set C 35
3.4 Positive definite matrices 36
Exercise set D 37
3.5 The normal distribution 38
Exercise set E 39
3.6 If you want a book on matrix algebra 40
4 Multiple Regression
4.1 Introduction 41
Exercise set A 44
4.2 Standard errors 45
Things we dont need 49
Exercise set B 49
4.3 Explained variance in multiple regression 51
Association or causation? 53
Exercise set C 53
4.4 What happens to OLS if the assumptions break down? 53
4.5 Discussion questions 53
4.6 End notes 59
5 Multiple Regression: Special Topics
5.1 Introduction 61
5.20LSisBLUE 61
Exercise set A 63
5.3 Generalized least squares 63
Exercise set B 65
5.4 Examples on GLS 65
Exercise set C 66
5.5 What happens to GLS if the assumptions break down? 68
5.6 Normal theory 68
Statistical significance 70
Exercise set D 71
5.7 The F-test 72
The F-test in applied work 73
Exercise set E 74
5.8 Data snooping 74
Exercise set F 76
5.9 Discussion questions 76
5.10 End notes 78
6 Path Models
6.1 Stratification 81
Exercise set A 86
6.2 Hookes law revisited 87
Exercise set B 88
6.3 Political repression during the McCarthy era 88
Exercise set C 90
TABLE OF CONTENTS
6.4 Inferring causation by regression 91
Exercise set D 93
6.5 Response schedules for path diagrams 94
Selection vs intervention 101
Structural equations and stable parameters 101
Ambiguity in notation 102
Exercise set E 102
6.6 Dummy variables 103
Types of variables 104
6.7 Discussion questions 105
6.8 End notes 112
7 Maximum Likelihood
7.1 Introduction 115
Exercise set A 119
7.2 Probit models 121
Why not regression? 123
The latent-variable formulation 123
Exercise set B 124
Identification vs estimation 125
What if the Ui are N(/z, tr2)? 126
Exercise set C 127
7.3 Logit models 128
Exercise set D 128
7.4 The effect of Catholic schools 130
Latent variables 132
Response schedules 133
The second equation 134
Mechanics: bivariate probit 136
Why a model rather than a cross-tab? 138
Interactions 138
More on table 3 in Evans and Schwab 139
More on the second equation 139
Exercise set E 140
7.5 Discussion questions 141
7.6 End notes 150
8 The Bootstrap
8.1 Introduction 155
Exercise set A 166
8.2 Bootstrapping a model for energy demand 167
Exercise set B 173
8.3 End notes 174
9 Simultaneous Equations
9.1 Introduction 176
Exercise set A 181
9.2 Instrumental variables 181
Exercise set B 184
9.3 Estimating the butter model 184
Exercise set C 185
9.4 What are the two stages? 186
Invariance assumptions 187
9.5 A social-science example: education and fertility 187
More on Rindfuss et al 191
9.6 Covariates 192
9.7 Linear probability models 193
The assumptions 194
The questions 195
Exercise set D 196
9.8 More on IVLS 197
Some technical issues 197
Exercise set E 198
Simulations to illustrate IVLS 199
9.9 Discussion questions 200
9.10 End notes 207
10 Issues in Statistical Modeling
10.1 Introduction 209
The bootstrap 211
The role of asymptotics 211
Philosophers stones 211
The modelers response 212
10.2 Critical literature 212
10.3 Response schedules 217
10.4 Evaluating the models in chapters 7-9 217
10.5 Summing up 218
References 219
Answers to Exercises 235
TABLE OF CONTENTS
The Computer Labs 294
Appendix: Sample MATLAB Code 310
Reprints
Gibson on McCarthy 315
Evans and Schwab on Catholic Schools 343
Rindfuss et al on Education and Fertility 377
Schneider et al on Social Capital 402
Index 431
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