Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.
"What I liked most with this book was the
comprehensive treatment of the practical application of GLMs,
covering most outcomes an applied statistician will encounter, and
at the same time presenting just enough of the necessary
theoretical basis for the discussed methods. Combined with the
thorough discussion of the R output, the text will serve as a
useful guide for the reader when applying the methods to his or her
own data set."
"The second edition of book 'Extending the linear model with R'
by Julian Faraway is an easily readable and relatively thorough
(without being theory heavy) sequel of the earlier 'Linear Models
with R' by the same author. The book itself is written in a
self-paced tutorial style in easily digestible chunks integrating
descriptions of underlying methodology, with data analysis and R
code. The organization of the book is well thought through. The
flow of the book is problem driven rather than driven by the
underlying statistical theory . . . the second edition is more
polished in terms of the figures used, R code and output display
and a crisper typesetting of equations."
-John T. Ormerod, University of Sydney
Praise for the First Edition:
"... well-written and the discussions are easy to follow ... very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics."
-Computational Statistics, April 2009, Vol. 24
"The text is well organized and carefully written ... provides
an overview of many modern statistical methodologies and their
applications to real data using software. This makes it a useful
text for practitioners and graduate students alike."
-Journal of the American Statistical Association, December 2007, Vol. 102, No. 480
"I enjoyed this text as much as [Faraway's Linear Models with
R]. The book is recommended as a textbook for a computational
statistical and data mining course including GLMs and
non-parametric regression, and will also be of great value to the
applied statistician whose statistical programming environment of
choice is R."
-Journal of Applied Statistics, July 2007, Vol. 34, No. 5
"This is a very pleasant book to read. It clearly demonstrates
the different methods available and in which situations each one
applies. It covers almost all of the standard topics beyond linear
models that a graduate student in statistics should know. It also
includes discussion of topics such as model diagnostics, rarely
addressed in books of this type. The presentation incorporates an
abundance of well-chosen examples ... this book is highly
-Biometrics, December 2006
"It has been a great pleasure to review this book, which
delivers both a readily accessible and reader-friendly account of a
wide range of statistical models in the context of R software.
Since the publication of the very well received first edition of
the book, R has considerably expanded both in popularity and in the
number of packages available. The second editionof the book takes
advantage of the greater functionality available now in R, and
substantially revises and adds several new topics."
-Andrzej Galecki, The International Biometric Society