Books like Introduction to linear regression analysis by Douglas C. Montgomery

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

Applied Linear Statistical Models – a comprehensive textbook by John Neter that covers linear models, regression analysis, analysis of variance, and experimental design for statisticians and researchers.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

xiv, 930 p. : 27 cm

A practical guide to regression and linear modeling that covers ordinary least squares, logistic, ordinal, Poisson, and loglinear techniques with a focus on assumptions, data quality, and nonlinear extensions.

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.

An accessible introduction to logistic regression models, updated and expanded with new topics and applications.