Overview

For those lacking the necessary mathematical foundations of applied multivariate analysis, this book presents the relevant aspects of transformational geometry, matrix algebra, and calculus. In particular, it illustrates major concepts in matrix algebra, linear structures, and eigenstructures geometrically, numerically, and algebraically. The authors emphasize the applications of these techniques by discussing potential solutions to problems outlined early in the book. They occasionally sketch proofs but usually present small numerical examples of the various concepts. The book provides a technical base for tackling most applications-oriented multivariate texts and provides a geometric perspective for developing an intuitive grasp of multivariate methods. This revised edition brings up-to-date many of the terms that describe the mathematical concepts and their operations. It also more clearly defines the relevance of the exercises to concerns within the business community and the social and behavioural sciences.

Full Product Details

9780121609559

Table of Contents

Reviews

This revision includes an update of terminology and basic mathematical concepts necessitated by the increasing use of multivariate techniques in a wide range of applied fields. It is highly recommended as a companion text for courses in multivariate methods and theory. --JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION [The book's] approach is unique and should be an interesting and effective way to learn basic linear algebra, even for some who are primarily interested in linear algebra for its own sake. --CHOICE It provides a careful and thorough introduction to vectors and matrices. Especially valuable is the material providing geometric interpretations...A particular strength of the book is the frequent use of small numerical examples which, for example, actually demonstrate the useful properties of determinants, and make absolutely clear what is meant by operations like the multiplication of matrices. The book is designed for readers who have no prior knowledge of matrix theory, and specifically for students in the behavioural and administrative sciences. However, it is also very clear and useful that it has material of value to anyone using multivariate methods. It should be on the reading list for all courses on multivariate analysis. --B.J.T. Morgan, Univeristy of Kent, Canterbury, U.K. in SHORT BOOK REVIEWS, December 1998

Author Information

J. Douglas Carroll is the Board of Governor's Professor of Marketing and Psychology in the Graduate School of Management at Rutgers, the State University of New Jersey. He holds a Ph.D. in mathematics from Princeton University. Dr. Carroll has published widely on multidimensional scaling and related techniques of data analysis. He is a member of several professional organizations.

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