Abstract
Multivariate analysis deals with issues related to the observations of many, usually correlated, variables on units of a selected random sample. These units can be of any nature such as persons, cars, cities, etc. The observations are gathered as vectors; for each selected unit corresponds a vector of observed variables. An understanding of vectors, matrices, and, more generally, linear algebra is thus fundamental to the study of multivariate analysis. Chapter 1 represents our selection of several important results on linear algebra. They will facilitate a great many of the concepts in multivariate analysis. A useful reference for linear algebra is Strang (1980).
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© 1999 Springer-Verlag New York, Inc.
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(1999). Linear algebra. In: Theory of Multivariate Statistics. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-22616-3_1
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DOI: https://doi.org/10.1007/978-0-387-22616-3_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98739-2
Online ISBN: 978-0-387-22616-3
eBook Packages: Springer Book Archive