Books like A course in linear models by Anant M. Kshirsagar

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 offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

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.

A popular statistical text, now updated and better than ever!

A comprehensive guide to Bayesian estimation techniques in mathematical statistics, with applications in various distributions and regression analysis.

A comprehensive guide to mathematical statistics for students and professionals alike, covering a wide range of topics in thirty-five chapters of varying sizes.

A Beginner's Guide to GAMM with R is the third in Highland Statistics' Beginner's Guide series, following the well-received A Beginner's Guide to Generalized Additive Models with R and A Beginner's Guide to GLM and GLMM with R. In this book we take the reader on an exciting voyage into the world of generalized additive mixed effects models (GAMM). Keywords are GAM, mgcv, gamm4, random effects, Poisson and negative binomial GAMM, gamma GAMM, binomial GAMM, NB-P models, GAMMs with generalized extreme value distributions, overdispersion, underdispersion, two-dimensional smoothers, zero-inflated GAMMs, spatial correlation, INLA, Markov chain Monte Carlo techniques, JAGS, and two-way nested GAMMs. The book includes three chapters on the analysis of zero-inflated data. Across the book frequentist approaches (gam, gamm, gamm4, lme4) are compared with Bayesian techniques (MCMC in JAGS and INLA). Datasets on squid, polar bears, coral reefs, ruddy turnstones, parasites in anchovy, common guillemots, harbor porpoises, forestry, brood parasitism, maximum cod length, and Common Scoters are used in case studies. The R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage.