Linear Models and Regression with R: An Integrated Approach

The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.

This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:

• Applications with data sets, and their implementation in R,
• Comprehensive coverage of regression diagnostics and model building,
• Coverage of other special topics such as collinearity, stochastic and inequality constraints, misspecified models, etc.,
• Use of simple statistical ideas and interpretations to explain advanced concepts, and simpler proofs of many known results,
• Discussion of models covering mixed-effects/variance components, spatial, and time series data with partially unknown dispersion matrix,
• Thorough treatment of the singular linear model, including the case of multivariate response,
• Insight into updates in the linear model, and their connection with diagnostics, design, variable selection, Kalman filter, etc.,
• Extensive discussion of the foundations of linear inference, along with linear alternatives to least squares.