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.
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