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May 25, 2026
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PSY 7110 - Multivariate Statistics
Introduction to multivariate statistics. Topics covered are matrix algebra, multiple regression, canonical correlation, discriminant analysis and classification, and factor analysis. Variety of commercial computer programs used.
Requisites: PSY 6112
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,PR,WP,WF,WN,FN,AU,I
Learning Outcomes:
- Compute an eigenvalue-eigenvector pair.
- Define and apply the basic operations of matrix algebra.
- Describe conceptually the connection between the eigenvalues and eigenvectors of the correlation matrix and min/max solutions in the multivariate calculus.
- Describe procedurally and explain conceptually the following multivariate methods: principle component analysis, factor analysis, multiple linear regression, discriminant analysis, canonical correlation, and multidimensional scaling.
- Explain how to measure distances in multidimensional metric spaces.
- Explain the meaning of an eigenvalue-eigenvector pair and its relevancy to multivariate analysis.
- Know how to apply these methods to diverse data sets with the aid of a computer program of choice and to explain the limits and appropriateness of applying them to psychological data.
- Represent multivaritate data visually.
- Represent systems of linear equations in terms of matrix equations.
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