This long-awaited revision combines a theoretical and data analytic approach to the subject, whilst emphasising modern developments in the field. Topics covered include econometrics, principal component analysis, factor analysis, canonical correlation analysis, discriminate analysis, cluster analysis, multi-dimensional scaling and directional data. Several methods of presentation, which helped make the first edition popular with professional statisticians and students alike, are used. For example, the data matrix is emphasised throughout, and density-free approach is given to normal theory. Tests are constructed using the likelihood ratio principle and the union intersection principle, and graphical methods are used in explanation. This work combines theoretical and data analytic approach. Appendices provide background of matrix algebra, univariate statistics and statistical tables. It offers a concise presentation.
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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.About the Author:
Edited by K. V. Mardia
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