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Applied multivariate statistics with R

Zelterman, Daniel.

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Applied multivariate statistics with R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied multivariate statistics with R/ by Daniel Zelterman.
作者:	Zelterman, Daniel.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xix, 463 p. :ill. (some col.), digital ;24 cm.
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.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-13005-2
ISBN:	9783031130052

Applied multivariate statistics with R
Zelterman, Daniel.

Applied multivariate statistics with R[electronic resource] /by Daniel Zelterman. - Second edition. - Cham :Springer International Publishing :2022. - xix, 463 p. :ill. (some col.), digital ;24 cm. - Statistics for biology and health,2197-5671. - Statistics for biology and health..

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.

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.

ISBN: 9783031130052

Standard No.: 10.1007/978-3-031-13005-2doiSubjects--Topical Terms:

677684
Multivariate analysis
--Data processing.

LC Class. No.: QA278 / .Z45 2022

Dewey Class. No.: 519.53502855133

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505 0 $a 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.
520 $a 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.
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