Multivariate Statistical Methods
Prerequisites: STAT 308 or STAT 408.
The course emphasizes both the theoretical foundations and applied aspects of multivariate analysis. Students will examine data exploration, inference, and model-based approaches, including principal components, factor and canonical correlation analysis, discriminant and cluster analysis, MANOVA, and modern dimensionality reduction techniques. The course will also introduce extensions of these methods and encourage critical evaluation of their assumptions and limitations. Statistical software, such as R, will be used for implementation and analysis.
Outcomes: By the end of the course students will be able to: explain and analyze the theoretical framework underlying common multivariate methods; derive and interpret results from principal component and factor analyses; apply and critique modern dimensionality reduction and visualization techniques.
The course emphasizes both the theoretical foundations and applied aspects of multivariate analysis. Students will examine data exploration, inference, and model-based approaches, including principal components, factor and canonical correlation analysis, discriminant and cluster analysis, MANOVA, and modern dimensionality reduction techniques. The course will also introduce extensions of these methods and encourage critical evaluation of their assumptions and limitations. Statistical software, such as R, will be used for implementation and analysis.
Outcomes: By the end of the course students will be able to: explain and analyze the theoretical framework underlying common multivariate methods; derive and interpret results from principal component and factor analyses; apply and critique modern dimensionality reduction and visualization techniques.