Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R , Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.

New to this edition are chapters devoted to longitudinal studies and theclustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.

Offers an applied approach to multivariate statistics Introduces R and draws upon R for statistical analyses and computing Contains exercises, full code to carry out practical examples, and selected solutions in an appendix

Autorentext

Daniel Zelterman is professor in the department of biostatistics at Yale University. His research areas include computational statistics, models for discrete valued data, and the design of clinical trials in cancer studies. In his spare time he plays oboe and bassoon in amateur orchestral groups and has backpacked hundreds of miles of the Appalachian Trail.

Inhalt
Chapter 1. Introduction.- Chapter 2. Elements of R.- Chapter 3. Graphical Displays.- Chapter 4. Basic Linear Algebra.- Chapter 5. The Univariate Normal Distribution.- Chapter 6. Bivariate Normal Distribution.- Chapter 7. Multivariate Normal Distribution.- Chapter 8. Factor Methods.- Chapter 9. Multivariate Linear Regression.- Chapter 10. Discrimination and Classification.- Chapter 11. Clustering Methods.- Chapter 12. Basic Models for Longitudinal Data.- Chapter 13. Time Series Models.- Chapter 14. Other Useful Methods.