Reduce the Dimensionality of Your Data
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 an exploratory data analysis tool and is also used for making predictive models.
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.
Figure 5.1 Example of Principal Components