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 OLS is BLUE

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 parameters

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 Introdcution

Exercise set A

7.2 Probit models

Why not regression?

The latent-variable formulation

Exercise set B

Identification vs estimation

What if the *U*_{i} are *N*(μ,σ^{2})?

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-tab?

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 and McCarthy

Evans and Schwab on Catholic Schools

Rindfuss et al on Education and Fertility

Schneider et al on Social Capital

Index