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Multivariate analysis

Multivariate Analysis deals with observations on more than one variable where there is some inherent interdependence between the variables. With several texts already available in this area, one may very well enquire of the authors as to the need for yet another book. Most of the available books fall into two categories, either theoretical or data analytic. The present book not only combines the two approaches but it also has been guided by the need to give suitable matter for the beginner as well as illustrating some deeper aspects of the subject for the research worker. Practical examples are kept to the forefront and, wherever feasible, each technique is motivated by such an example.

Multivariate analysis

The second part of a two-part work on multivariate analysis is concerned with the more specialized techniques that follow on from the basic theory presented in part one. Topics covered include discriminant analysis, cluster analysis, path analysis, graphical modelling, latent variable techniques, repeated measures analysis and growth curve models. Modern problems and techniques, such as handling of high dimensional data and the use of neural networks are featured and the book concludes with a discussion of strategic aspects of multivariate analysis.This first volume of a two part work on multivariate analysis concentrates on what might be termed the 'basic' techniques: topics covered range from initial inspection and graphical display of multivariate data through informal data-based methods such as principal components, canonical variates and multi-dimensional scaling, to inferential procedures such as two-sample hypothesis tests and the multivariate general linear model. The latter procedures are based on the multivariate normal distribution, so a comprehensive chapter on distribution theory lays the necessary foundations. Whilst full technical details are supplied, the emphasis throughout is on providing a readble and 'user-friendly' presentation.

Multivariate analysis