Textbook in PDF format Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting. Frontmatter Fundamentals Overview Data and measurement Some basic methods in mathematics and probability Statistical inference Simulation Linear regression Background on regression modeling Linear regression with a single predictor Fitting regression models Prediction and Bayesian inference Linear regression with multiple predictors Assumptions, diagnostics, and model evaluation Transformations and regression Generalized linear models Logistic regression Working with logistic regression Other generalized linear models Before and after fitting a regression Design and sample size decisions Poststratification and missing-data imputation Causal inference Causal inference and randomized experiments Causal inference using regression on the treatment variable Observational studies with all confounders assumed to be measured Additional topics in causal inference What comes next? Advanced regression and multilevel models Appendixes A - Computing in R B - 10 quick tips to improve your regression modeling References Author Index Subject Index