Forewarning- I'm not a very advanced in regression. (Updated with 2 edits)
I'm running multiple regressions with Excel and noticed that my p-values are becoming insignificant when adding more variables. My model is very simple. N=9 with a statistically significant variable, then I add another variable (which by itself is significant) and the p-value jumps into non-significant land.
I've read that this could be because of multicollinearity: should I be concerned with this since I'm only using the model to predict? How do I confirm this in Excel, and if so is my model not valid?
Edit - Thanks for your input guys:
My process is to test each variable one by one, and if it registers a significant p value (less than .05) and a high R2'd then I keep it in the model and add another variable.
This is where I am getting confused, as I add another variable the R2 (and adjusted R2) increase but the p values both increase above .05 (but independently are less that .05).
What does this mean? Is there any way to run a good multiple regression model in excel using a small sample size (N of 9-15) for prediction without the above problem.
Edit #2 - I read through some of the other threads and a recurring theme is that this happens b/c of collinearity. I did a VIF test and the value is 1.97 which is below 2.5 so doesn't set off any alarms. So if my two variables don't have collinearity, whats happening to the p values? i.e. both are significant independently but only 1 is when I regress both variables?