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