Applied Multivariate Analysis

Catalog description

A detailed exposition of some of the more commonly used multivariate statistical techniques, including the geometric intuition underlying their use. Familiarity with the notation and the basic operations of matrix algebra, and with the standard univariate statistical procedures is assumed. Topics include dimension reduction techniques (principal components, factor analysis, and canonical correlation), clustering, classification, neural network, and structural equation models. Typically offered Fall Spring. Prerequisite(s): STAT 43000 FOR LEVEL UG WITH MIN. GRADE OF C- (MAY BE TAKEN CONCURRENTLY) AND STAT 34600 FOR LEVEL UG WITH MIN. GRADE OF C- Course Learning Outcomes 1. Investigate multivariate data descriptive techniques. 2. Analyze multivariate random variables. 3. Use multivariate techniques such as principal component analysis, factor analysis and cluster analysis. 4. Use statistical software for data analysis and clearly communicate the results. View Class Schedule