Multivariate Statistical Methods: A First Course

Multivariate Statistical Methods: A First Course

Multivariate Statistical Methods: A First Course

Multivariate Statistical Methods: A First Course

Synopsis

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis.

An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is pointing out the analogy between a common univariate statistical technique and the corresponding multivariate method. Many computer examples--drawing on SAS software --are used as demonstrations.

Throughout the book, the computer is used as an adjunct to the presentation of a multivariate statistical method in an empirically oriented approach. Basically, the model adopted in this book is to first present the theory of a multivariate statistical method along with the basic mathematical computations necessary for the analysis of data. Subsequently, a real world problem is discussed and an example data set is provided for analysis. Throughout the presentation and discussion of a method, many references are made to the computer, output are explained, and exercises and examples with real data are included.

Excerpt

The purpose of this book is to introduce multivariate statistical methods to advanced undergraduate and graduate business students, although students of other disciplines will also find the book useful. The material presented is suitable for a one-semester introductory course and provides coverage of what we believe to be the most commonly used multivariate techniques. The book is not intended to be particularly comprehensive; rather, the intention is to keep mathematical details to a minimum while conveying the basic principles of multivariate statistical methods.

Like most academic authors, our choice of the material in the book and mode of presentation are a joint product of our research and our teaching. One way to articulate the rationale for the mode of presentation is to draw a distinction between mathematical statisticians who gave birth to the field of multivariate statistics, like Hotelling or Wilks, and those who focus on methods for data analysis and the interpretation of results. Possibly the distinction between Pythagoreans (mathematicians) and Archimedeans (scientists) is useful, as long as one does not assume that Pythagoreans are not interested in data analysis and Archimedeans are not interested in contributing to the mathematical foundations of their discipline. We certainly feel more comfortable as Archimedeans, although we occasionally indulge in Pythagorean thinking. Therefore, this book is primarily written for individuals concerned with data analysis, although true expertise requires familiarity with both approaches.

It is assumed that readers already have a working knowledge of introductory statistics, particularly tests of significance using the normal, t, F, and chi-square distributions, single and multiple factor analysis of variance, and . . .

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