This textbook focuses on regression and its application to real, complex statistical problems. It is written by experienced authors and provides practical tools for comparison, estimation, prediction, and causal inference. Rather than extensive theory, the emphasis is on implementable methods and computer code, especially in R and Stan.
The book covers relevant topics such as sample size, missing data, and various techniques, with examples from practice. The transition to logistic regression and Generalized Linear Models (GLMs) is carefully developed. Figures and clear presentations support understanding of models and their applications in experiments and observational studies.