Applied multivariate statistics with R / Daniel Zelterman.
- 作者： Zelterman, Daniel.
- 其他作者：
- 其他題名：
- Statistics for biology and health.
- 出版： Cham, Switzerland : Springer c2022.
- 叢書名： Statistics for biology and health
- 主題： Multivariate analysis--Data processing. , R (Computer program language)
- 版本：2nd ed.
- ISBN： 9783031130045: EUR89.99 、 3031130049 (hbk.)
- 一般註：Previous edition: 2015.
- 書目註：Includes bibliographical references and index.
-
讀者標籤：
- 系統號： 005286605 | 機讀編目格式
館藏資訊
摘要註
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 the clustering 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.
內容註
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.