Data Analysis and Graphics Using R - An Example-Based Approach

Informationen zum Autor John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the 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. Klappentext 3rd edition. Hands-on guide for scientists, students and practising statisticians. Zusammenfassung This third edition of the popular guide to using R reflects recent improvements to the R system! including major advances in graphical user interfaces and graphics packages. It emphasizes hands-on analysis! graphical display and interpretation of data. Ideal for researchers! students of applied statistics! and practising statisticians. Inhaltsverzeichnis 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.

Product details

• Authors: W. John Braun, W. John (University of Western Ontario) Braun, John Maindonald, John (Australian National University Maindonald
• Publisher: Cambridge University Press Academic
• Languages: English
• Product format: Hardback
• Released: 06.05.2010
• EAN: 9780521762939
• ISBN: 978-0-521-76293-9
• Dimensions: 183 mm x 260 mm x 30 mm
• Series: Cambridge Series in Statistical and Probabilistic Mathematics; Cambridge Series in Statistical and Probabilistic Mathematics; Cambridge Series in Statistica
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics