Data Analysis and Graphics Using R
Data Analysis and Graphics Using R
W. John Braun, University of Western Ontario
$119.00
USDDiscover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), 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 third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
- 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
"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 News
"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
Product details
June 2010Hardback
9780521762939
549 pages
260 × 183 × 30 mm
1.3kg
150 b/w illus. 12 colour illus. 40 tables
Available
Table of Contents
- Preface
- Content - how the chapters fit together
- 1. A brief introduction to R
- 2. Styles of data analysis
- 3. Statistical models
- 4. A review of inference concepts
- 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
- 15. Graphs in R
- Epilogue
- Index of R symbols and functions
- Index of authors.
