Hi there
I have a set of results and 4 "candidate" explanatory variables. Those variables are correlated beteween each other (only two of them are not correlated with one another). What I want to figure out is wich one(s) of them is (are) the best at explaining the results.
I understand stepwise regression is screwed by the multicolinearity (I tried to run it and it all went fine until I tried to put the interactions in the mix)
I tried an ANOVA, two of them are significant, but I get NaNs when I ask about interactions.
I tried to run a PCA among all the explanatory variables but 1) i don't understand how the PCA isnt concerned with the results I am trying to explain and 2) I don't understand the results I am getting with pcacov: what do those coefficients in the matrix mean ? How am I supposed to rank the variables ?
Does it make sense ? Thank you very much ps: i also learned about the Akaike information cirterium but i am unsure how this would apply here. I hope something more simple could help me because it feels like trying to crush a fly with a bulldozer
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