Data Analysis and Graphics Using R
An Example-based Approach
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- John Maindonald, Australian National University, Canberra
- John Braun, University of Western Ontario
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Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.Read more
- Practical, hands-on, example-based approach deals with real-world issues
- Extensive use of graphs for exploration of data and interpretation of analyses
- R code, data sets, updates and exercise solutions, all provided on companion website
Reviews & endorsements
From reviews of previous edition: "The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses. 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. I give it a strong recommendation to the scientist or data analyst who wishes to an easy-to-read and an understandable reference on the use of R for practical data analysis."
R NewsSee more reviews
From reviews of previous edition: "The text includes a wealth of practical examples, drawn from a variety of practical applications which should be easily understood by the reader. The methods demonstrated are suitable for use in areas such as biology, social science, medicine and engineering. The core of the book is taken up with detailed discussion of regression methods which leads onto more advanced statistical concepts."
ISI Short Book Reviews
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 page of resources complements the book's usefulness for a road audience of statisticians and practitioners."
From Previous Edition: "Provide considerable insight into very powerful procedures."
A. Ralph Henderson, Clinical Chemistry
"There are many books published in applied statistics that explain the R language. However, the book under review stands out due to its versatility and because it is easy to follow and understand the context."
Ita Cirovic Donev, The Mathematical Association of America
"...A gentle tour guide for new R users, aiming to help them navigate through many powerful tools that the open source R system offers."
Zhaohui Steve Qin, Center for Statistical Genetics, BioInformatics
"The style of the book is a commendable "learn by example" - each of the many statistical techniques is centered on real-world examples. The collective of topics is eclectic and the book also comes with extensive R code."
Carl James Schwarz, Biometrics
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- Edition: 2nd Edition
- Date Published: April 2007
- format: Adobe eBook Reader
- isbn: 9780511247941
- contains: 12 colour illus. 50 tables 150 exercises
- availability: This item is not supplied by Cambridge University Press in your region. Please contact eBooks.com for availability.
Table of Contents
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
Index of R symbols and functions
Index of terms
Index of names.
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