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.Content:
Chapter 1 Introduction (pages 1โ4):
Chapter 2 Reduction of Dimensionality (pages 5โ61):
Chapter 3 Development and Study of Multivariate Dependencies (pages 62โ80):
Chapter 4 Multidimensional Classification and Clustering (pages 81โ138):
Chapter 5 Assessment of Specific Aspects of Multivariate Statistical Models (pages 139โ226):
Chapter 6 Summarization and Exposure (pages 227โ318):