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Publisher:

Cambridge University Press

Publication Date:

2009

Number of Pages:

442

Format:

Paperback

Price:

40.00

ISBN:

9780521743853

Category:

Textbook

[Reviewed by , on ]

Fernando Q. Gouvêa

05/26/2009

This is a revised edition of a book first published in 2005. See our review of that edition. According to Freedman's preface, "the exposition has been improved in a number of ways, without (I hope) introducing new difficulties. There are many new examples and exercises." In addition, some of the material has been reorganized; a look at the table of contents shows that these changes are minor.

What the table of contents does not show, however, is that solutions to the problems and the reprints of social science papers fill up half the book. The goal is definitely to have the intended readers (upper-level undergraduates and graduate students in statistics) interact directly with the data analysis in the examples.

One distinguishing feature of this book is the choice of examples: many of them deal with fairly controversial socio-political issues, presumably in the hope of engaging the students. Thus, the author discusses a study of which segments of society supported political repression in the McCarthy era, another suggesting that Catholic schools are much more effective than public schools, and so on. Instructors who are leery of getting into political discussions should probably pick another book!

Fernando Q. Gouvêa is Carter Professor of Mathematics at Colby College in Waterville, ME.

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

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

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