One danger with applied books such as this is that they become recipe lists of the kind 'press this key to get that result.' This is not so with Faraway's book. Throughout, it gives plenty of insight on what is going on, with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen
I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models
I find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught.
-Journal of the Royal Statistical Society
Overall, Linear Models with R is well written and, given the increasing popularity of R, it is an important contribution.
-Technometrics, Vol. 47, No. 3, August 2005
There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysis
The text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations.
-Short Book Reviews of the International Statistical
The book is very comprehensibly written and can therefore be recommended for beginners in linear models. It is clearly and simply explained how to use R and which packages are necessary to analyze linear models.
All in all, this book is recommendable as a textbook for computational linear regression courses and therefore for students and lecturers, but also for applied statisticians who want to get started on regression analysis using the software R.
Dr Faraway uses many examples and graphical procedures to illustrate the methods. This is a great strength of the book.
Linear Models with R is one of several books appearing to make R more accessible by bringing together functions from a number of packages and illustrating their use. From this perspective alone it is an important contribution.
I feel this book does a nice job of describing the methods available in linear modeling and illustrating the realistic implementation of these methods in a careful data analysis.
-Statistics in Medicine, 2006