Often in experimental design multiple variables are related in such a way that by analyzing them simultaneously additional information, and often times essentially information, can be gathered that would be missed if each variable was examined individually (as is the case in univariate analyses). For example, in univariate statistics we study random variables that have a normal distribution (characterized by the usual bell shaped curve), while in multivariate statistics we study groups of random variables that have a multivariate normal distribution. Such variables are related in a way that the effects can’t be meaningfully interpreted separately.
- Descriptive Multivariate Statistics
- Multivariate Normal Distribution
- Hotelling’s T2 Statistic and Analysis of the Mean
- Multivariate Analysis of Variance (MANOVA)
- Box’s Test for the Equality of Covariance Matrices
- Factor Analysis
- Cluster Analysis