I have a dataset with 60ish variables. I performed a PCA (4 components) to reduce the dimensionality. The total variance explained by the 4 principal components is 0.79
When I compute the correlations between my original variables and the 4 PC I find that two variables are very highly correlated with PC1 and PC2 (around 0.95) and only few variables are moderately correlated with PC3 & PC4 (around 0.6)
My question is what about the other variables ? From a statistics perspective, does it mean that they don't really matter as they don't explain much ?
Thanks in advance.