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 DEPARTMENT OF MATHEMATICS
 AND STATISTICS
1999 - 2000 MINICALENDAR
 Faculty of Arts
 Faculty of Pure and Applied Science

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AS/SC/ MATH 4630 3.0 W
GS/MATH 6625 3.0

Applied Multivariate Statistical Analysis

      The course covers the basic theory of the multivariate normal distribution and its application to multivariate inference about a single mean, comparison of several means and multivariate linear regression. As time and interest permit, further related topics may also be covered.
      We will study methods of analysis for data which consist of observations on a number of variables. The primary aim will be interpretation of the data, starting with the multivariate normal distribution and proceeding to the standing multivariate inference theory based on linear models. Sufficient theory will be developed to facilitate an understanding of the main ideas. This will necessitate a good background in matrix algebra, and some knowledge of vector spaces as well. Computers will be used extensively, and familiarity with elementary use of SAS or S+ will be assumed. Topics covered will include the multivariate normal population, inference about means and covariance, multivariate linear models, principal component analysis, , and some discussion of canonical correlation analysis, discriminant and classification, factor analysis and cluster analysis, as time permits.
      Grades will be based on a combination of class quizzes and a final examination, plus homework including a group project. The coordinator may permit students to enrol who have background "equivalent to" the formal prerequisites below.

Prerequisite:AS/SC/AK/MATH 3131 3.0; AS/SC/MATH 3034 3.0 or AS/SC/MATH 3230 3.0; AS/SC/MATH 2022 3.0 or AS/SC/AK/MATH 2222 3.0.

Coordinator: M. Asgharian

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York University
N520 Ross Building, 4700 Keele Street
Toronto, Ontario, Canada M3J 1P3
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