# About stepwise regression and correlation

I am trying to fit a model to some observed data with the least squares method. Now, I am at the stage where I have run a stepwise regression (traditional), with Entry level $=0.025$ and Stay level $=0.025$ (I really don't know what thresholds to use, this is my first model I am trying to fit).

Anyhow, I am getting a pretty good fit - if one should rely on $R^2 = 0.98%$. However I can see from scatterplots for the remaining $5$ predictors that there seem to be some correlation between some of the predictors. So, I decided to check Pearsons coefficients ($\rho$) which gave the result that the predictors $x_1$ and $x_2$ ($\rho=0.98%$, P value $<10^{-3}$ ), $x_3$ and $x_4$ ($\rho=0.94%$, P value < $10^{-3}$ )were strongly correlated.

My questions are:

1. Is it possible for two variables that have so strong correlation to survive in a stepwise regression?
2. How do I proceed from here? How do I know which of the predictors to omit?

(If more information is needed in order to give a good answer please let me know so that i can give that information).

• Can you explain what SLENTRY=0.025 and SLSTAY=0.025 means? – Jeremy Miles Nov 25 '14 at 0:23
• i've changed it .... – Danny Nov 25 '14 at 0:28
• I still don't know what it means. Is that a p-value? – Jeremy Miles Nov 25 '14 at 0:52
• They can survive because the data say that they can survive (based on your criteria, which is p-value, I think?). You are using an automatic algorithm to build a model. If you think you should use an automatic algorithm it's a bit strange to not like the result and remove a variable. – Jeremy Miles Nov 25 '14 at 0:53