Applied Multivariate Statistics with R

  • Daniel┬áZelterman

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Daniel Zelterman
    Pages 1-15
  3. Daniel Zelterman
    Pages 17-53
  4. Daniel Zelterman
    Pages 55-87
  5. Daniel Zelterman
    Pages 89-116
  6. Daniel Zelterman
    Pages 117-150
  7. Daniel Zelterman
    Pages 151-172
  8. Daniel Zelterman
    Pages 173-205
  9. Daniel Zelterman
    Pages 207-229
  10. Daniel Zelterman
    Pages 231-256
  11. Daniel Zelterman
    Pages 257-286
  12. Daniel Zelterman
    Pages 287-313
  13. Daniel Zelterman
    Pages 315-338
  14. Daniel Zelterman
    Pages 339-360
  15. Back Matter
    Pages 361-393

About this book


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. 


R software clustering factor methods graphical displays linear algebra linear regression matrix algebra biostatistics normal distribution statistical inference for biology time series models

Authors and affiliations

  • Daniel┬áZelterman
    • 1
  1. 1.School of Public HealthYale UniversityNew HavenUSA

Bibliographic information

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