Overview
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Table of contents (11 chapters)
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Front Matter
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Introduction
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Front Matter
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Finding new underlying variables
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Front Matter
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Procedures based on the multivariate normal distribution
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Front Matter
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Multidimensional scaling and cluster analysis
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Front Matter
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Back Matter
Keywords
About this book
This book provides an introduction to the analysis of multivariate data. It should be suitable for statisticians and other research workers who are familiar with basic probability theory and elementary inference, and also have a basic grounding in matrix algebra. The book should also be suitable as a text for undergraduate and postgraduate statistics courses on multivariate analysis. The book covers a wider range oftopics than some other books in this area. It deals with preliminary data analysis, principal component and factor analysis and traditional normal-theory material. It also covers cluster analysis and scaling techniques. In writing the book, we have tried to provide a reasonable blend of theory and practice, in contrast to much of the existing literature which concentrates on one or other aspect. Enough theory is given to introduce the concepts and to make the topics mathematically interesting. But we also discuss the use (and misuse) of the techniques in practice and present appropriate real-life examples. Although the book is primarily an introductory text, we have nevertheless added appropriate references for further reading, so that the reader may extend his studies if he wishes.
Authors and Affiliations
Bibliographic Information
Book Title: Introduction to Multivariate Analysis
Authors: Christopher Chatfield, Alexander J. Collins
DOI: https://doi.org/10.1007/978-1-4899-3184-9
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: C. Chatfield and A.J. Collins 1980
Softcover ISBN: 978-0-412-16030-1Published: 01 January 1980
eBook ISBN: 978-1-4899-3184-9Published: 11 November 2013
Edition Number: 1
Number of Pages: X, 246
Topics: Statistical Theory and Methods