I have a set of 20 variables that measure different aspects of objects. All variables are real numbers. My intent is to investigate how much these variables are different and what are the specific set of

*n*variables that are less correlated with the remaining

*20-n.*

The problem with Spearman and similar is that it is just about pair. The problem with PCA is that the first component is of 17 variables and hence I do not know for example which are the set of 3 variables that are more different than the remaining 17.

The problem with Spearman and similar is that it is just about pair. The problem with PCA is that the first component is of 17 variables and hence I do not know for example which are the set of 3 variables that are more different than the remaining 17.

To better explain my problem, suppose I have the following 4 variables related to a car: price, HP, MPG, Insurance cost, and the number of seconds for 0to60. How can I find the set of 3 variables less correlated to the remaining 2?