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. Prerequisite: MATH 342 with C or better. Recommended: DA 460.
After completing this class, students should be able to:
- 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
- Winter 2020 (current quarter)