DA 410 - Multivariate Analysis

5 CR

Introduce various statistical methods for analyzing more than one outcome variable and understanding the relationships between variables. Topics include a variety of multivariate models such as MANOVA, discriminant functions, canonical correlation, and cluster analysis. The focus will be on real world examples from a variety of sources and using statistical software.

Recommended: DA 460.
Prerequisite(s): MATH 342 with C or better.

Course Outcomes
- Identify the common multivariate analysis methods, and their advantages and limitations. - Evaluate the relevant aspects of a real world data set and choose an appropriate type of multivariate analysis method - Formulate, fit, and apply models using

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