On the one hand, a principal component analysis is used to reduce the data volume. On the
other, it makes it possible to extract hypothetical variables which can be used to characterize
a data set. It groups together covariant variables of a data set that can be interpreted as
a single factor. PCA is a secondary transform. In other words, it stores its results in the current
workspace as secondary history files.