Use principal component analysis to summarize and visualize the structure of data described by several quantitative variables, while obtaining the uncorrelated factors between them. These factors may be used as new variables which allows you to:
- avoid multicolinearity in multiple regression or in discriminant analysis,
- perform cluster analysis while considering only essential information, i.e. by keeping the primary factors only.
Rotations can be applied on the factors. Several methods are available including Varimax, Quartimax, Equamax, Parsimax, Quartimin and Oblimin and Promax.