1. Introduction to multivariate analysis
1.1 Introduction
1.2 Basic concepts of matrix algebra
1.3 Description of multivariate data: data matrix, mean vector, covariance matrix, and correlation matrix
1.4 Representation of multivariate data
2. Multivariate normal distribution
2.1 Basic properties
2.2 Simulation methods
2.3 Application examples
3. Regression analysis
3.1 Simple linear regression
3.2 Multiple linear regression
3.3 Model validation
3.4 Application examples
4. Generalized linear models
4.1 Logistic regression
4.2 Poisson regression
4.3 Application examples
5. Principal components
5.1 Motivation and construction
5.2 Standardized case
5.3 Examples with data
6. Factor analysis
6.1 Orthogonal factorial model
6.2 Estimation and factor rotation
6.3 Application examples
7. Cluster analysis
7.1 Proximity measures
7.2 Hierarchical clustering: Ward's method
7.3 Non-hierarchical clustering: K-means method
7.4 Application examples with data
