The underlying, mathematical objective in principal components analysis is to obtain:
A) Correlated linear combinations of the original variables that account for as much of the total variance in the original variables as possible.
B) Uncorrelated linear combinations of the original variables that account for some of the total variance in the original variables.
C) Uncorrelated linear combinations of the original variables that account for as much of the total variance in the original variables as possible.
D) Uncorrelated combinations of the original variables that account for as much of the total variance in the original variables as possible.

Question

Quizplus · Accepted Answer

Principal Components Analysis (PCA) aims to reduce the dimensionality of a data set by transforming the original variables into a new set of uncorrelated variables (principal components) that account for as much of the variance in the data as possible.

The Underlying, Mathematical Objective in Principal Components Analysis Is to Obtain