Applied Regression Analysis and Generalized Linear Models

1. Statistical Models and Social Science
2. What Is Regression Analysis?
3. Examining Data
4. Transforming Data
5. Linear Least-Squares Regression
6. Statistical Inference for Regression
7. Dummy-Variable Regression
8. Analysis of Variance
9. Statistical Theory for Linear Models*
10. The Vector Geometry of Linear Models*
11. Unusual and Influential Data
12. Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
13. Collinearity and Its Purported Remedies
14. Logit and Probit Models for Categorical Response Variables
15. Generalized Linear Models
16. Time-Series Regression and Generalized Least Squares*
17. Nonlinear Regression
18. Nonparametric Regression
19. Robust Regression*
20. Missing Data in Regression Models
21. Bootstrapping Regression Models
22. Model Selection, Averaging, and Validation
23. Linear Mixed-Effects Models for Hierarchical and Longitudinal Data
24. Generalized Linear and Nonlinear Mixed-Effects Models