Linear Model and Extensions

The linear model and its extensions play fundamental roles in both theoretical and applied statistics, due to their transparency and interpretability in modeling empirical data. This textbook, based on the author’s course on linear modeling at UC Berkeley taught over the past ten years, only requires basic knowledge of linear algebra, probability theory, and statistical inference. It assumes minimal knowledge of linear modeling, and reviews basic linear algebra, probability, and statistics in the appendix. It covers linear regression, logistic regression, Poisson regression, generalized estimating equation, quantile regression, and Cox regression, which are widely used statistical models across many areas. It balances rigorous theory, simulation, and data analysis.

Key Features:
All R code and data sets available at Harvard Dataverse Includes over 200 exercises Solutions manual available for instructors, upon request from the author
This book is suitable for advanced undergraduate or graduate-level courses on linear modeling, or graduate-level courses on generalized linear modeling. It can also be used as a reference for researchers who are searching for basic properties of the linear model and its extensions.

Peng Ding is a Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications.