If you want to see the arrangement of points across many correlated variables, you can use principal component analysis to show the most prominent directions of the high-dimensional data. Using principal component analysis reduces the dimensionality of a set of data. Principal components is a way to picture the structure of the data as completely as possible by using as few variables as possible.
For n original variables, n principal components are formed as follows:
• The first principal component is the linear combination of the standardized original variables that has the greatest possible variance.
• Each subsequent principal ...