Fox J. Applied Regression Analysis and Generalized Linear Models

Los Angeles: SAGE, 2017. — 817 p.Linear models, their variants, and extensions—the most important of which are generalized linear models—are among the most useful and widely used statistical tools for social general research. This book aims to provide an accessible, in-depth, modern treatment of regression analysis, linear models, generalized linear models, and closely related methods.
The book should be of interest to students and researchers in the social sciences. Although the specific choice of methods and examples reflects this readership, I expect that the book will prove useful in other disciplines that employ regression models for data analysis and in courses on applied regression and generalized linear models where the subject matter of applications is not of special concern.About the Author
Statistical Models and Social Science
Data Craft
What Is Regression Analysis?
Examining Data
Transforming Data
Linear Models And Least Squares
Linear Least-Squares Regression
Statistical Inference for Regression
Dummy-Variable Regression
Analysis of Variance
Statistical Theory for Linear Models
The Vector Geometry of Linear Models
Linear-Model Diagnostics
Unusual and Influential Data
Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
Collinearity and Its Purported Remedies
Generalized Linear Models
Logit and Probit Models for Categorical Response Variables
Generalized Linear Models
Extending Linear And Generalized Linear Models
Time-Series Regression and Generalized Least Squares
Nonlinear Regression
Nonparametric Regression
Robust Regression
Missing Data in Regression Models
Bootstrapping Regression Models
Model Selection, Averaging, and Validation
Mixed-Effects Models
Linear Mixed-Effects Models for Hierarchical and Longitudinal Data
Generalized Linear and Nonlinear Mixed-Effects Models
Appendix A