The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of measured variables that capture as much of the variability in the original variables as possible. Principal component analysis is a dimension-reduction technique. It can be used as an exploratory data analysis tool, but is also useful for constructing predictive models, as in principal components analysis regression (also known as PCA regression or PCR).
For data with a very large number of variables, the Principal Components platform provides an estimation method called the Wide method that enables you to calculate principal components in short computing times. These principal components can then be used in PCA regression.
The Principal Components platform also supports factor analysis. JMP offers several types of orthogonal and oblique factor analysis-style rotations to help interpret the extracted components. For factor analysis, see the Factor Analysis chapter in the Consumer Research book.