Zelterman Applied Multivariate Statistics with R

1. Auflage 2015
ISBN: 978-3-319-14093-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 393 Seiten, eBook

Reihe: Statistics for Biology and Health

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Zelterman, Daniel

Inhaltsverzeichnis

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.

Über Autor(innen)

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.