Principal Component Analysis (PCA)

Principal component analysis is used to determine many variables and determine which variables that fit best for the subject. PCA is used for the subject when there is linear correlation between the variables.

The PCA investigates the multiple variables in more than two axis, so you can choose the best axis that represent your variables with indication on the % for the Axis selected. This plot shows Axis 1 (65%) and Axis 2 (20%) with a total of 85% from the variables used.