Bulletin Description
Prerequisite: Graduate standing; upper-division standing with MATH 441 or equivalent; or permission of the instructor.
Multivariate Statistical Methods are used to analyze the joint behavior of more than one random variable. There are a number of multivariate techniques available including Factor Analysis, Principle Component Analysis, Canonical Correlation, Multidimensional Scaling, MANOVA, and Discriminant Analysis.
Topics
- Matrix Algebra and Random Vectors
- Multivariate Normal Distribution
- Multivariate Statistics
- Factor Analysis
- Principle Component Analysis
- Cluster Analysis
- Canonical Correlation
- Multidimensional Scaling
- MANOVA
- Discriminant Analysis
Student Learning Outcomes
Upon completion of this course a student should be able to
- Build and interpret models using multivariate Normal distribution
- Perform essential Principal Component Analysis, Factor Analysis, Cluster Analysis, and Structural Equation Modeling
- Implement essential multivariate analyses in a professional statistical package R/SAS
- Perform independent multivariate analysis projects and write project reports
- Independently build multivariate analysis proficiency using professional literature
Evaluation of Students
Students will be graded on quizzes, homework assignments, data analysis project,and examinations.
Textbooks & Software
- Applied Multivariate Statistical Analysis, 6th Edition, Johnson, Wichern
- SAS, SAS Corporation
- R, by R Development Core Team