Introduction. Estimation. Inference. Prediction. Explanation. Diagnostics. Problems with the Predictors. Problems with the Error. Transformation. Model Selection. Shrinkage Methods. Insurance Redlining—A Complete Example. Missing Data. Categorical Predictors. One Factor Models. Models with Several Factors. Experiments with Blocks. Appendix: About R. Bibliography. Index.
Julian J. Faraway
"After 10 years, a new edition of Faraway’s excellent Linear Models
with R is now available.. . There are several major changes in this
edition. The material on interpreting linear models has been
reorganized to emphasize the distinction between prediction and
explanation; this was done with the addition of two new chapters .
. . Several other chapters benefit from the addition of new
material. . . Finally, most chapters conclude with more exercises
than in the previous edition."
—The American Statistician, 2016"This book is a must-have tool for
anyone interested in understanding and applying linear models. The
logical ordering of the chapters is well thought out and portrays
Faraway’s wealth of experience in teaching and using linear models.
… The reorganization of the material in this second edition
presents linear models with R in a coherent and easy-to-follow way.
In summary, this book provides an excellent basis for understanding
and applying linear models. It lays down the material in a logical
and intricate manner and makes linear modeling appealing to
researchers from virtually all fields of study."
—Biometrical Journal, 2015"The book provides an excellent
introduction of the various aspects of linear models with many
interesting examples.
The explanations are clear enough for beginners with little
statistical background and are accompanied by worked examples with
associated R code. This is an important contribution since it
provides readers/students an opportunity to replicate the analyses
and results of an example. There are many books written on the
topic of linear models, but this book takes an applied approach and
explains the concepts intuitively using graphical explanations and
examples.
Overall, this is a nicely written book, which can lay a strong
foundation for senior undergraduate and beginning graduate
students. This book can be recommended as a textbook for
computational linear regression courses. It will also be useful for
practitioners who want to get started on applying regression models
for studying associations among different variables, estimation of
regression coefficients, and prediction. It offers insightful
interpretations and discussions with examples worked using the R
software."
—MAA Reviews, January 2015Praise for the First Edition:"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
"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 Institute
"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."
-Biometrics
"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
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