Methods for Statistical Data Analysis of Multivariate Observations

Gnanadesikan, R.

ISBN 10: 0471308455 / ISBN 13: 9780471308454

Published by John Wiley & Sons, 1977

About this Item

Hardcover; Wiley Series in Probability and Mathematical Statistics; surplus library copy with the usual stampings; reference number written on spine; fading and shelf wear to exterior; otherwise in good condition with clean text, firm binding. Seller Inventory # 69546

Bibliographic Details

Title: Methods for Statistical Data Analysis of ...

Publisher: John Wiley & Sons

Publication Date: 1977

Binding: Hardcover

Condition: UsedGood

About this title

Synopsis:

A practical guide for multivariate statistical techniques–– now updated and revised

In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data–based interpretations relevant to multivariate analysis in a way that addresses real–world problems arising in many areas of interest.

Greatly revised and updated, this Second Edition provides helpful examples, graphical orientation, numerous illustrations, and an appendix detailing statistical software, including the S (or Splus) and SAS systems. It also offers
∗ An expanded chapter on cluster analysis that covers advances in pattern recognition
∗ New sections on inputs to clustering algorithms and aids for interpreting the results of cluster analysis
∗ An exploration of some new techniques of summarization and exposure
∗ New graphical methods for assessing the separations among the eigenvalues of a correlation matrix and for comparing sets of eigenvectors
∗ Knowledge gained from advances in robust estimation and distributional models that are slightly broader than the multivariate normal
This Second Edition is invaluable for graduate students, applied statisticians, engineers, and scientists wishing to use multivariate techniques in a variety of disciplines.

Review:

"...second edition of the book first published in 1977...new material appears in virtually every chapter..." ( Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001)