# 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. – David Lane Oct 7 '17 at 15:54