Skip to main content

Multivariate Analysis and Pattern Recognition

  • Chapter
Modern Applied Statistics with S-PLUS

Part of the book series: Statistics and Computing ((SCO))

  • 901 Accesses

Abstract

Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending on whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer Science+Business Media New York

About this chapter

Cite this chapter

Venables, W.N., Ripley, B.D. (1999). Multivariate Analysis and Pattern Recognition. In: Modern Applied Statistics with S-PLUS. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3121-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-3121-7_11

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4757-3123-1

  • Online ISBN: 978-1-4757-3121-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics