Applied Multivariate Statistics

Undergraduate

Matrix algebra and random vector, sample geometry and random sampling, multivariate normal distribution, inference about mean, comparison of several multivariate means, multivariate linear regression model, principle components, factor analysis, covariance analysis, canonical correlation analysis, discrimination and classification, cluster analysis, computational techniques and use of SAS, Maple or other statistical packages and related topics. Course Format: Lectures, 3 hours per week; lab 1 hour per week. Prerequisite(s): STAT (MATH) 1P11 or 1P12; STAT 2P82 (MATH) or permission of the instructor Course Notes: This course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term. Exclusions: Completion of this course will replace previously assigned grade and credit obtained in MATH 3P86.