Applied Regression Analysis and Generalized Linear Models, 3rd ed.
Fox, John

1. Statistical Models and Social Science 
1.1 Statistical Models and Social Reality	 
1.2 Observation and Experiment	 
1.3 Populations and Samples	  

I. DATA CRAFT

2. What Is Regression Analysis? 
2.1 Preliminaries	 
2.2 Naive Nonparametric Regression	 
2.3 Local Averaging	   

3. Examining Data 
3.1 Univariate Displays	 
3.2 Plotting Bivariate Data	 
3.3 Plotting Multivariate Data	   

4. Transforming Data 
4.1 The Family of Powers and Roots	 
4.2 Transforming Skewness	 
4.3 Transforming Nonlinearity	 
4.4 Transforming Nonconstant Spread	 
4.5 Transforming Proportions	 
4.6 Estimating Transformations as Parameters*	  

II. LINEAR MODELS AND LEAST SQUARES

5. Linear Least-Squares Regression 
5.1 Simple Regression	 
5.2 Multiple Regression	   

6. Statistical Inference for Regression 
6.1 Simple Regression	 
6.2 Multiple Regression	 
6.3 Empirical Versus Structural Relations	 
6.4 Measurement Error in Explanatory Variables*	   

7. Dummy-Variable Regression 
7.1 A Dichotomous Factor	 
7.2 Polytomous Factors	 
7.3 Modeling Interactions	   

8. Analysis of Variance 
8.1 One-Way Analysis of Variance	 
8.2 Two-Way Analysis of Variance	 
8.3 Higher-Way Analysis of Variance	 
8.4 Analysis of Covariance	 
8.5 Linear Contrasts of Means	   

9. Statistical Theory for Linear Models* 
9.1 Linear Models in Matrix Form	 
9.2 Least-Squares Fit	 
9.3 Properties of the Least-Squares Estimator	 
9.4 Statistical Inference for Linear Models	 
9.5 Multivariate Linear Models	 
9.6 Random Regressors	 
9.7 Specification Error	 
9.8 Instrumental Variables and Two-Stage Least Squares	   

10. The Vector Geometry of Linear Models* 
10.1 Simple Regression	 
10.2 Multiple Regression	 
10.3 Estimating the Error Variance	 
10.4 Analysis-of-Variance Models	  

III. LINEAR-MODEL DIAGNOSTICS
  
11. Unusual and Influential Data 
11.1 Outliers, Leverage, and Influence	 
11.2 Assessing Leverage: Hat-Values	 
11.3 Detecting Outliers: Studentized Residuals	 
11.4 Measuring Influence	 
11.5 Numerical Cutoffs for Diagnostic Statistics	 
11.6 Joint Influence	 
11.7 Should Unusual Data Be Discarded?	 
11.8 Some Statistical Details*	   

12. Non-Normality, Nonconstant Error Variance, Nonlinearity 
12.1 Non-Normally Distributed Errors	 
12.2 Nonconstant Error Variance	 
12.3 Nonlinearity	 
12.4 Discrete Data	 
12.5 Maximum-Likelihood Methods*	 
12.6 Structural Dimension	   

13. Collinearity and Its Purported Remedies 
13.1 Detecting Collinearity	 
13.2 Coping With Collinearity: No Quick Fix	  

IV. GENERALIZED LINEAR MODELS

14. Logit and Probit Models for Categorical Response Variables 
14.1 Models for Dichotomous Data	 
14.2 Models for Polytomous Data	 
14.3 Discrete Explanatory Variables and Contingency Tables	   

15. Generalized Linear Models 
15.1 The Structure of Generalized Linear Models	 
15.2 Generalized Linear Models for Counts	 
15.3 Statistical Theory for Generalized Linear Models*	 
15.4 Diagnostics for Generalized Linear Models	 
15.5 Analyzing Data From Complex Sample Surveys	  

V. EXTENDING LINEAR AND GENERALIZED LINEAR MODELS

16. Time-Series Regression and Generalized Leasr Squares* 
16.1 Generalized Least-Squares Estimation	 
16.2 Serially Correlated Errors	 
16.3 GLS Estimation With Autocorrelated Errors	 
16.4 Correcting OLS Inference for Autocorrelated Errors	 
16.5 Diagnosing Serially Correlated Errors	 
16.6 Concluding Remarks	   

17. Nonlinear Regression 
17.1 Polynomial Regression	 
17.2 Piece-wise Polynomials and Regression Splines	 
17.3 Transformable Nonlinearity	 
17.4 Nonlinear Least Squares*	   

18. Nonparametric Regression 
18.1 Nonparametric Simple Regression: Scatterplot Smoothing	 
18.2 Nonparametric Multiple Regression	 
18.3 Generalized Nonparametric Regression	   

19. Robust Regression* 
19.1 M Estimation	 
19.2 Bounded-Influence Regression	 
19.3 Quantile Regression	 
19.4 Robust Estimation of Generalized Linear Models	 
19.5 Concluding Remarks	   

20. Missing Data in Regression Models 
20.1 Missing Data Basics	 
20.2 Traditional Approaches to Missing Data	 
20.3 Maximum-Likelihood Estimation for Data Missing at Random*	 
20.4 Bayesian Multiple Imputation	 
20.5 Selection Bias and Censoring	   

21. Bootstrapping Regression Models 
21.1 Bootstrapping Basics	 
21.2 Bootstrap Confidence Intervals	 
21.3 Bootstrapping Regression Models	 
21.4 Bootstrap Hypothesis Tests*	 
21.5 Bootstrapping Complex Sampling Designs	 
21.6 Concluding Remarks	   

22. Model Selection, Averaging, and Validation 
22.1 Model Selection	 
22.2 Model Averaging*	 
22.3 Model Validation	  

VI. MIXED-EFFECT MODELS

23. Linear Mixed-Effects Models for Hierarchical and Longitudinal Data 
23.1 Hierarchical and Longitudinal Data	 
23.2 The Linear Mixed-Effects Model	 
23.3 Modeling Hierarchical Data	 
23.4 Modeling Longitudinal Data	 
23.5 Wald Tests for Fixed Effects	 
23.6 Likelihood-Ratio Tests of Variance and Covariance Components	 
23.7 Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models	 
23.8 BLUPs	 
23.9 Statistical Details*	   

24. Generalized Linear and Nonlinear Mixed-Effects Models 
24.1 Generalized Linear Mixed Models	 
24.2 Nonlinear Mixed Models	  

Appendix A
 
References
 
Author Index
 
Subject Index
 
Data Set Index