Applied Multivariate Statistics with R

Daniel Zelterman (Autor)

Buch | Softcover

XVI, 393 Seiten

2016 | 1. Softcover reprint of the original 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-36163-5 (ISBN)

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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.

Daniel Zelterman, PhD, 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.

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 is so clearly explained with R code throughout that it could be used as a self-learning text for an applied multivariate course and should be assigned as a selflearning adjunct assignment for a graduate level theoretical multivariate course. The real-word examples are page turners and ubiquitous use of color and fancy graphs easily explained make this usually dry topic an exciting one." (Donna Pauler Ankerst, Biometrics, Vol. 73 (1), March, 2017)

"This book demonstrates the process and outcomes for a wide array of multivariate statistical applications using program R. ... The chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. This book is strongly recommended for graduate-level statistics practitioners." (Hemang B. Panchal, Doody's Book Reviews, December, 2015)

Erscheinungsdatum	06.11.2018
Reihe/Serie	Statistics for Biology and Health
Zusatzinfo	XVI, 393 p. 121 illus., 108 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	731 g
Themenwelt	Informatik ► Weitere Themen ► Bioinformatik
	Mathematik / Informatik ► Mathematik ► Statistik
	Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie
Schlagworte	Clustering • factor methods • graphical displays • linear algebra • linear regression • matrix algebra biostatistics • Normal distribution • R software • Statistical Inference for Biology • systems biology • time series models
ISBN-10	3-319-36163-5 / 3319361635
ISBN-13	978-3-319-36163-5 / 9783319361635
Zustand	Neuware