Название: Linear Models with R, 3rd Edition
Автор: Julian J. Faraway
Издательство: CRC Press
Год: 2025
Страниц: 388
Язык: английский
Формат: pdf, epub (true)
Размер: 11.6 MB

A Hands-On Way to Learning Data Analysis.

Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.

This book is written in three languages: English, Mathematics and R. My motivation in writing the first edition of this text was to combine these three seamlessly to allow coherent exposition of the practice of linear modeling. This was uncommon in the past, but this has become popular in the now large number of statistics books that integrate R. Clearly it is a method that works, but it does require the reader to become somewhat fluent in R. This is not a book about learning R but like any foreign language, one becomes proficient by practicing it rather than by memorizing the dictionary. The reader is advised to look elsewhere for a basic introduction to R but should not hesitate to dive into this book and pick it up as you go. I shall try to help. The base graphics system in R is fine for producing standard plots. A useful alternative is provided by the ggplot2 package, which you will need to install as described in Appendix A.

New to the Third Edition:

40% more content with more explanation and examples throughout
New chapter on sampling featuring simulation-based methods
Model assessment methods discussed
Explanation chapter expanded to include introductory ideas about causation
Model interpretation in the presence of transformation
Crossvalidation for model selection
Chapter on regularization now includes the elastic net
More on multiple comparisons and the use of marginal means
Discussion of design and power

Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.