A PRIMER FOR MULTIVARIATE INFERENCE.
PORTLAND UNIV OR
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This report is a first version of an interim text for social science graduate students and pre-graduate majors. It requires some mathematical aptitude but no mathematical training beyond high-school mathematics. The first chapter is a review of the fundamentals of statistical inference, up through the multivariate normal distribution. The second chapter is an introduction to linear algebra, with heavy emphasis on determinants and the relationships between determinants, multivariate statistics and their geometric representation. There are numerous examples. Interpretation of the evaluation by Gauss method is stressed. Chapter 3 presents tests of linear hypotheses and the Markoff Theorem, with numerous examples. Chapter 4 very briefly develops the multivariate normal linear model one example is presented. Author
- Statistics and Probability