# How to solve multicollinearity in OLS regression with correlated dummy variables and collinear continuous variables?

I have predicted an ecological variable using OLS regression which showed the model accounts for more than 72% of the variance in the dependent variable (DV). However, I am also interested in which covariates have much impact on the DV. But I found that some of the independent variables are collinear making the Variance Inflation Factor > 10. What is the solution to the multicollinearity problem both for the continuous variables Temp and Vapor ( Drop one? ) and the categorical variables 1-8 which have very high VIF?

 Variable           Coeff.  Std Coeff.  VIF    Std Error    t      P -Value
Constant          -0.228   0            0      0.086       -2.644  0.008
Precipitation      <.001   0.151       2.688   <.001        8.541  0.0
Solar Rad          0.002   0.343       2.836   <.001        18.939 <.001
Temp              -0.116  -1.604       28.12   0.004       -28.11  0.0
Water Stress       0.881   0.391       2.352   0.037        23.7   <.001
Vapor Pressure     0.135   1.382       30.49   0.006        23.259 0.0
1               -0.103   -0.109      52.086  0.074       -1.398  0.162
2               -0.14    -0.048      6.49    0.079       -1.761  0.078
3               -0.11    -0.048      10.007  0.077       -1.42   0.156
4               -0.104   -0.234      236.288 0.073       -1.416  0.157
5               -0.097   -0.242      285.244 0.073       -1.331  0.183
6               -0.104   -0.09       35.067  0.074       -1.406  0.16
8               -0.119   -0.261      221.361 0.073       -1.629  0.103
ELEVATION        <.001   -0.115      3.917   <.001       -5.381  <.001

Condition Number: 59.833
Mean of Correlation Matrix: 0.221
1st Eigenvalue divided by m: 0.328


• I would pay more attention to the standard errors of the coefficients than the VIF. After all, the main problem with multicolliniariy is that it increases standard errors. Commented Oct 7, 2017 at 15:54