Regression and Other Stories

Most of this book is devoted to examples and tools for the practical use and understanding of regression models, starting with linear regression with a single predictor and moving to multiple predictors, nonlinear models, and applications in prediction and causal inference. In this chapter, we lay out some of the mathematical structure of inference for regression models and some algebra to help you understand estimation for linear regression. We also explain the rationale for the use of the Bayesian fitting routine stan_glm and its connection to classical linear regression. This chapter thus provides background and motivation for the mathematical and computational tools used in the rest of the book.