统计模型 理论和实践 英文版·第2版【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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1 Observational Studies and Experiments1.1 Introduction1

1.2 The HIP trial4

1.3 Snow on cholera6

1.4 Yule on the causes of poverty9

Exercise set A13

1.5 End notes14

2 The Regression Line18

2.1 Introduction18

2.2 The regression line18

2.3 Hooke's law22

Exercise set A23

2.4 Complexities23

2.5 Simple vs multiple regression26

Exercise set B26

2.6 End notes28

3 Matrix Algebra29

3.1 Introduction29

Exercise set A30

3.2 Determinants and inverses31

Exercise set B33

3.3 Random vectors35

Exercise set C35

3.4 Positive definite matrices36

Exercise set D37

3.5 The normal distribution38

Exercise set E39

3.6 If you want a book on matrix algebra40

4 Multiple Regression41

4.1 Introduction41

Exercise set A44

4.2 Standard errors45

Things we don't need49

Exercise set B49

4.3 Explained variance in multiple regression51

Association or causation?53

Exercise set C53

4.4 What happens to OLS if the assumptions break down?53

4.5 Discussion questions53

4.6 End notes59

5 Multiple Regression:Special Topics5.1 Introduction61

5.2 OLS is BLUE61

Exercise set A63

5.3 Generalized least squares63

Exercise set B65

5.4 Examples on GLS65

Exercise set C66

5.5 What happens to GLS if the assumptions break down?68

5.6 Normal theory68

Statistical significance70

Exercise set D71

5.7 The F-test72

"The" F-test in applied work73

Exercise set E74

5.8 Data snooping74

Exercise set F76

5.9 Discussion questions76

5.10 End notes78

6 Path Models81

6.1 Stratification81

Exercise set A86

6.2 Hooke's law revisited87

Exercise set B88

6.3 Political repression during the McCarthy era88

Exercise set C90

6.4 Inferring causation by regression91

Exercise set D93

6.5 Response schedules for path diagrams94

Selection vs intervention101

Structural equations and stable parameters101

Ambiguity in notation102

Exercise set E102

6.6 Dummy variables103

Types of variables104

6.7 Discussion questions105

6.8 End notes112

7 Maximum Likelihood115

7.1 Introduction115

Exercise set A119

7.2 Probit models121

Why not regression?123

The latent-variable formulation123

Exercise set B124

Identification vs estimation125

What if the Ui are N(μ,σ2)?126

Exercise set C127

7.3 Logit models128

Exercise set D128

7.4 The effect of Catholic schools130

Latent variables132

Response schedules133

The second equation134

Mechanics:bivariate probit136

Why a model rather than a cross-tab?138

Interactions138

More on table 3 in Evans and Schwab139

More on the second equation139

Exercise set E140

7.5 Discussion questions141

7.6 End notes150

8 The Bootstrap155

8.1 Introduction155

Exercise set A166

8.2 Bootstrapping a model for energy demand167

Exercise set B173

8.3 End notes174

9 Simultaneous Equations176

9.1 Introduction176

Exercise set A181

9.2 Instrumental variables181

Exercise set B184

9.3 Estimating the butter model184

Exercise set C185

9.4 What are the two stages?186

Invariance assumptions187

9.5 A social-science example:education and fertility187

More on Rindfuss et al191

9.6 Covariates192

9.7 Linear probability models193

The assumptions194

The questions195

Exercise set D196

9.8 More on IVLS197

Some technical issues197

Exercise set E198

Simulations to illustrate IVLS199

9.9 Discussion questions200

9.10 End notes207

10 Issues in Statistical Modeling209

10.1 Introduction209

The bootstrap211

The role of asymptotics211

Philosophers'stones211

The modelers' response212

10.2 Critical literature212

10.3 Response schedules217

10.4 Evaluating the models in chapters 7-9217

10.5 Summing up218

References219

Answers to Exercises235

The Computer Labs294

Appendix:Sample MATLAB Code310

Reprints315

Gibson on McCarthy315

Evans and Schwab on Catholic Schools343

Rindfuss et al on Education and Fertility377

Schneider et al on Social Capital402

Index431