Question

The variation present in the PCs decrease as we move from the 1st PC to the last one.

Answer 1

It is important the variation present in the PC's to decrease and move for 1st PC's. So the output of PCA principal will have many components with numbers is equal to their original variables.

In this main process, it will have many useful properties and can be defined with a linear combination of optimally-weighted through an observed variables.

• The PC's are ready to discuss or orthogonal
• They are very essential with a linear combination. It is actually found and turns into eigenvector with squared fields with principles of least squares.
• The main important PC's are most useful sometime in outlier detection, regression, etc.