Books like Classical and Modern Regression with Applications (Duxbury Classic) by Raymond H. Myers

A practical guide to Bayesian statistical analysis that prioritizes computation, model checking, and data‑collection design, with examples spanning regression, hierarchical, robust, generalized linear, and mixture models.

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Applied Linear Regression, 3rd Edition by Sanford Weisberg guides students through theory and practice of linear regression, emphasizing model building, graphical diagnostics, and modern techniques like bootstrapping and non-linear extensions.

A practical guide for applying regression techniques to real data, illustrating common irregularities, assumptions, and evaluation steps with clear examples that can be run in standard statistical software.