Modern statistical software systems provide sophisticated tools for researchers who need to manipulate and display their data. Using such systems requires training both in the software itself and in the statistical methods that it relies on. Concentrating on the freely available R system, this book demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from the authors’ experience, both as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate statistics, whether an upper-graduate student, a researcher, or a practising scientist or statistician. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.
• Practical, example-based guide to statistical methods based on authors’ teaching and consulting experience • Conveniently-supplied computer code makes it easy for readers to rework all examples; data sets, and scripts that can be used to run the calculations, will be available from the Web • Starts with basic concepts, but proceeds to modern methods and ideas, with minimum of mathematics, by linking statistical analysis closely with graphical presentation
Contents
Introduction; 1. A brief introduction to R; 2. Styles of data analysis; 3. Statistical models; 4. Introduction to formal inference; 5. Regression with a single predictor; 6. Multiple linear regression; 7. Exploiting the linear model framework; 8. Logistic regression and other generalised linear models; 9. Multi-level models, time series and repeated measures; 10. Tree-based classification and regression; 11. Multivariate data exploration and discrimination; 12. The R system - additional topics; 13. Epilogue - models; Appendix: S-plus differences; Bibliography; Acknowledgements; Index.
Reviews
‘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 News
‘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 complement the book’s usefulness for a road audience of statisticians and practitioners.’ Biometrics
'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 page of resources complement the book's usefulness for a broad audience of statisticians and practitioners.' Journal of the American Statistical Association
'… includes a wealth of practical examples, drawn from a variety of practical applications which should be easily understood by the reader.' Publication of the International Statistical Institute
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