统计模型 : 理论和实践

Foreword to the ReVised Edition

Preface

1 Observational Studies and Experiments

1.1 Introduction

1.2 The HIP trial

1.3 Snow on cholera

1.4 Yule on the causes of poverty

Exercise set A

1.5 End notes

2 The Regression Line

2.1 Introduction

2.2 The regression line

2.3 Hooke's law

Exercise set A

2.4 Complexities

2.5 Simple vs multiple regression

Exercise set B

2.6 End notes

3 Matrix Algebra

3.1 Introduction

Exercise set A

3.2 Determinants and inverses

Exercise set B

3.3 Random vectors

Exercise set C

3.4 Positive definite matrices

Exercise set D

3.5 The normal distribution

Exercise set E

3.6 If you want a book on matrix algebra

4 Multiple Regression

4.1 Introduction

Exercise set A

4.2 Standard errors

Things we don't need

Exercise set B

4.3 Explained variance in multiple regression

Association or causation?

Exercise set C

4.4 What happens to OLS if the assumptions break down?

4.5 Discussion questions

4.6 End notes

5 Multiple Regression: Special Topics

5.1 Introduction

5.2 OLSisBLUE

Exercise set A

5.3 Generalized least squares

Exercise set B

5.4 Examples on GLS

Exercise set C

5.5 What happens to GLS if the assumptions break down?

5.6 Normal theory

Statistical significance

Exercise set D

5.7 The F-test

"The" F-test in applied work

Exercise set E

5.8 Data snooping

Exercise set F

5.9 Discussion questions

5.10 End notes

6 Path Models

6.1 Stratification

Exercise set A

6.2 Hooke's law revisited

Exercise set B

6.3 Political repression during the McCarthy era

Exercise set C

6.4 Inferring causation .by regression

Exercise set D

6.5 Response schedules for path diagrams

Selection vs intervention

Structural equations and stable parameter:Ambiguity in notation

Exercise set E

6.6 Dummy variables

Types of variables

6.7 Discussion questions

6.8 End notes

7 Maximum Likelihood

7.1 Introduction

Exercise set A

7.2 Probit models

Why not regression?

The latent-variable formulation

Exercise set B

Identification vs estimation

What if the Ui are N?

Exercise set C

7.3 Logit models

Exercise set D

7.4 The effect of Catholic schools

Latent variables

Response schedules

The second equation

Mechanics: bivariate probit

Why a model rather than a cross-lab?

Interactions

More on table 3 in Evans and Schwab

More on the second equation

Exercise set E

7.5 Discussion questions

7.6 End notes

8 The Bootstrap

8.1 Introduction

Exercise set A

8.2 Bootstrapping a model for energy demand

Exercise set B

8.3 End notes

9 Simultaneous Equations

9.1 Introduction

Exercise set A

9.2 Instrumental variables

Exercise set B

9.3 Estimating the butter model

Exercise set C

9.4 What are the two stages?

Invariance assumptions

9.5 A social-science example: education and fertility

More on Rindfuss et al

9.6 Covariates

9.7 Linear probability models

The assumptions

The questions

Exercise set D

9.8 More on IVLS

Some technical issues

Exercise set E

Simulations to illustrate IVLS

9.9 Discussion questions

9.10 End notes

10 Issues in Statistical Modeling

10.1 Introduction

The bootstrap

The role of asymptotics

Philosophers' stones

The modelers' response

10.2 Critical literature

10.3 Response schedules

10.4 Evaluating the models in chapters 7-9

10.5 Summing up

References

Answers to Exercises

The Computer Labs

Appendix: Sample MATLAB Code

Reprints

Gibson on McCarthy

Evans and Schwab on Catholic Schools

Rindfuss et al on Education and Fertility

Schneider et al on Social Capital

Index

《统计模型：理论和实践》是一本优秀的统计模型教材，着重讲解线性模型的应用问题，包括广义最小二乘和两步最小二乘模型，以及二分变量的probit及logit模型的应用，还包括关于研究设计、二分变量回归及矩阵代数的背景知识。
这还是一本鼓舞人心的而又易读的书，无论是老师还是学生都会从中受益。