Remedial Measures of multicollinearity

Remedial Measures of  multicollinearity:

Multicollinearity does not actually bias results; it just produces large standard errors in the related independent variables. With enough data, these errors will be reduced.
In a pure statistical sense multicollinearity does not bias the results, but if there are any other problems which could introduce bias multicollinearity can multiply ( by orders of magnitude ) the effects of that bias. More importantly, the usual use of regression is to take coefficients from the model and then apply them to other data. If the new data differs in any way from the data that was fitted we may introduce large errors in predictions because the pattern of multicollinearity between the independent variables is different in new data from the data used for your estimates. We try seeing what happens if we use independent subsets of your data for estimation and apply those estimates to the whole data set.
In addition, we may:

1) Leave the model as is, despite multicollinearity. The presence of multicollinearity doesn't affect the fitted model provided that the predictor variables follow the same pattern of multicollinearity as the data on which the regression model is based.

2) Drop one of the variables. An explanatory variable may be dropped to produce a model with significant coefficients. However, you lose information (because you've dropped a variable). Omission of a relevant variable results in biased coefficient estimates for the remaining explanatory variables.

3) Obtain more data. This is the preferred solution. More data can produce more precise parameter estimates (with lower standard errors).

4) mean-center the predictor variables. Mathematically this has no effect on the results from a regression. However, it can be useful in overcoming problems arising from rounding and other computational steps if a carefully designed computer program is not used.

5) Standardize your independent variables. This may help reduce a false flagging of a condition index above 30.