Preface; 1. A brief introduction to R; 2. Styles of data analysis; 3. Statistical models; 4. An introduction to formal inference; 5. Regression with a single predictor; 6. Multiple linear regression; 7. Exploiting the linear model framework; 8. Generalized linear models and survival analysis; 9. Time series models; 10. Multi-level models and repeated measures; 11. Tree-based classification and regression; 12. Multivariate data exploration and discrimination; 13. Regression on principal component or discriminant scores; 14. The R system - additional topics; Epilogue - models; References; Index of R symbols and functions; Index of terms; Index of names.
Hands-on guide to the R system for data analysis for scientists, students and practising statisticians.
John Maindonald is Visiting Fellow at the Mathematical Sciences Institute, Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health, to population genetics, machine learning, economic history, and forensic linguistics. John Braun is Associate Professor of Statistical and Actuarial Sciences, University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists and most recently has become involved with a network of forestry researchers.
From reviews of previous edition: 'I would strongly recommend the
book to scientists who have already had a regression or a linear
models course and who wish to learn to use R … an easy-to-read and
an understandable reference on the use of R for practical data
analysis.' R News
From reviews of previous edition: 'This book does an excellent job
of describing the basics of a variety of statistical tools, both
classical and modern, through examples from a wide variety of
disciplines … the book's writing style is very readable, with clear
explanations and precise introductions of all topics and
terminology … the book also provides a wealth of examples from
various physical and social sciences, engineering, and medicine
that have been effectively chosen to illustrate not only the basics
of the statistical methods, but also some of the interesting
subtleties of the analyses that may require careful interpretation
and discussion … I believe that they have … created a readable book
that is rich with clear explanations and illustrative examples of
the capability of a diverse set of tools. The packaging of the
material with the R language is natural, and the extensive web
pages of resources complement the book's usefulness for a road
audience of statisticians and practitioners.' Biometrics
From reviews of previous edition: 'This book does an excellent job
of describing the basics of a variety of statistical tools, both
classical and modern, through examples from a wide variety of
disciplines … With its focus on ideas and concepts, rather than an
extensive formula-based presentation, the book finds a nice balance
between discussing statistical concepts and teaching the basics of
the freely-available statistical package R … a readable book that
is rich with clear explanations and illustrative examples of the
capability of a diverse set of tools. The packaging of the material
with the R language is natural, and the extensive web pages of
resources complement the book's usefulness for a broad audience of
statisticians and practitioners.' Journal of the American
Statistical Association
'…a very useful book that can be recommended for applies
statisticians and other scientists that want to use R for data
analysis, and as a textbook for an applied statistics course using
R.' Journal of Applied Statistics
'… an excellent intermediate-level text … Though a bit more terse
than Dalgaard's Introductory Statistics with R, Maindonald and
Braun's exposition of the R language is nonetheless first rate.'
Steve Miller, DM Review Online
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