I have trouble making sense (i.e. real-world sense…) out of some of my results.
I have Y and X1 and X2 for different geographic areas. Meaning they are the same variables, but their actual values are different per area.
I did a multiple linear regression and the R-squared is good for all of them (F-test indicates significance).
I then calculated the semi-partial (or part) correlation to figure out the relative importance of X1 and X2 in explaining variance of Y. As I understand it, adding these two correlation coefficients squared, will add up to the overall R-squared - and if not, that "remainder" is variance redundantly predicted by X1 and X2.
For my 7 models, this redundancy varies from very little, to a lot: can anyone give me insights as to what that could imply about my X1, X2 and Y?
There's a lot of stuff out there on suppression (when the squared semi-partial correlation added together is larger than the overall R-squared), but I have not yet come across anything really useful about redundancy.
[I fear this might be a very basic statistical question, I apologize - but it makes me wonder why I can't find anything informative on the WWW!]