Statistics 5401
Multivariate Analysis

Here are copies of the class handouts (in PDF format).

• 1. Introduction
• 2. Matrix manipulation
• 3. Matrix multiplication
• 4. Graphics
• 5. Inverses and determinants
• 6. Geometry
• 7. Eigen structure
• 8. Variances
• 9. Multivariate Normal
• 10. Assessing Normality
• 11. Hotelling's T2
• 12. Simultaneous Confidence
• 13. Profile Analysis
• 14. Univariate Linear Models
• 15. Multivariate Linear Models
• 16. Testing Multivariate Linear Hypotheses
• 17. Repeated Measures
• 18. Principal Components
• 19. Biplots
• 20. Population Principal Components
• 21. Factor Models
• 22. Factor Extraction
• 23. Factor Rotation
• 22b. Factor Extraction addendum
• 23b. Factor Rotation addendum
• 23. Factor Scores
• 25. Canonical Correlation
• 26. Inference for Canonical Correlation
• 27. Density-based Classification
• 28. More Density-based Classification
• 29. More than two groups
• 30. Fisher discrimination
• 31. Distances
• 32. Hierarchical Clustering
• 33. K-means Clustering
• 34. Multidimensional Scaling