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Applied multivariate statistics with R

紀錄類型:	書目-語言資料,印刷品 : 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 : : 2015.,
面頁冊數:	xvi, 393 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Multivariate analysis. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-14093-3
ISBN:	9783319140933

Applied multivariate statistics with R
Zelterman, Daniel.

Applied multivariate statistics with R[electronic resource] /by Daniel Zelterman. - Cham :Springer International Publishing :2015. - xvi, 393 p. :ill., digital ;24 cm. - Statistics for biology and health,1431-8776. - Statistics for biology and health..

Introduction -- Elements of R -- Graphical Displays -- Basic Linear Algebra -- The Univariate Normal Distribution -- Bivariate Normal Distribution -- Multivariate Normal Distribution -- Factor Methods -- Multivariate Linear Regression -- Discrimination and Classification -- Clustering -- Time Series Models -- Other Useful Methods -- References -- Appendix -- Selected Solutions -- Index.

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor 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. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. .

ISBN: 9783319140933

Standard No.: 10.1007/978-3-319-14093-3doiSubjects--Topical Terms:

577402
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Applied multivariate statistics with R
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505 0 $a Introduction -- Elements of R -- Graphical Displays -- Basic Linear Algebra -- The Univariate Normal Distribution -- Bivariate Normal Distribution -- Multivariate Normal Distribution -- Factor Methods -- Multivariate Linear Regression -- Discrimination and Classification -- Clustering -- Time Series Models -- Other Useful Methods -- References -- Appendix -- Selected Solutions -- Index.
520 $a This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor 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. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. .
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