sorry not the smartest question, tried to google this and search forum, but questions remain.
So x1 correlates with y by r=0.8. If I now add x2 and x3 to the model, the whole model correlates with r=0.82 to y. So I only gain 0.02 of r by adding x2 and x3. So are x2 and x3 not important for the model?! What I would like to know which share of the r is explained by which variable?
Details: x2 and x3 both come with significant p values in the model. So this means it's highly probable that in the overall population x2 and x3 also would have an observable effect on y. But at the same time they only explain 0.02 of the r if I add them. How can both be true at the same time? x2 and x3 alone correlate with r=0.50 to y. So they seem to be important? x1 and x3 correlate by r=0.45 with each other. The other variables barely correlate with each other.
edit: My goal is to understand which variables out of 10 actually explain my dependent variable. After some more googling I understand that you absolutely can't use stepwise (forward or backwards) selection for this. Is that correct? If yes which method for variable selection is the next easiest to use? (preferably in excel)