# Considering correlation of independent variables in regression model

I have one dependent variable C and four independent variables:

• S
• SD
• TPA
• VPT

The correlation matrix between them is the following

    TPA     VPT      S
TPA -       -       -
VPT -0.37   -       -
S   0.04    0.00    -
SD  0.05    -0.04   0.01


TPAand VPT have a moderate negative linear relationship.

Now I want to create a regression model:

C ~ S + SD + VPT + TPA


Does it makes sense to consider the correlation between VPT and TPA in the model? If so, what should I add to it to consider the correlation?

Collinearity is a problem in regression. However, it is best assessed with condition indexes, not correlation matrices. If you are using R you can get these in the perturb package; if you are using SAS you can get them with the /collin option. Although I said that correlations aren't the best way to look at collinearity, I don't think this will be a problem here.