# Removing multi-collinearity with PCA for regression analysis

I'm interested in studying the impact or importance of each feature on the response variable.

I'm thinking running multiple linear regression with multiple features, and running regression analysis with t-test to test the significance of each regression coefficients. But I've read that in case of multi-collinearity, the result t-test on regression coefficients are unreliable.

Let's say that I initially have 6 features, then I apply PCA with n_components=6. In the other words, I'm not doing any dimensional reduction. I'm just removing correlations. When the correlation among features are removed with PCA, will the result of t-test on hte new correlation-removed features be reliable?

• yes it will. but those variables won't be the same as before. how much correlated are your variables? people tend to over-worry about collinearity – carlo Nov 7 '19 at 20:13