Abstract
In this book we present applied multivariate data analysis methods for making inferences regarding the mean and covariance structure of several variables, for modeling relationships among variables, and for exploring data patterns that may exist in one or more dimensions of the data. The methods presented in the book usually involve analysis of data consisting of n observations on p variables and one or more groups. As with univariate data analysis, we assume that the data are a random sample from the population of interest and we usually assume that the underlying probability distribution of the population is the multivariate normal (MVN) distribution. The purpose of this book is to provide students with a broad overview of methods useful in applied multivariate analysis. The presentation integrates theory and practice covering both formal linear multivariate models and exploratory data analysis techniques.
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© 2002 Springer-Verlag New York, Inc.
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(2002). Introduction. In: Timm, N.H. (eds) Applied Multivariate Analysis. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-22771-9_1
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DOI: https://doi.org/10.1007/978-0-387-22771-9_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95347-2
Online ISBN: 978-0-387-22771-9
eBook Packages: Springer Book Archive