Question

how do I adapt a backward selection approach to select the key independent variables for a model and also check multicollinearity issues for the selected variables

Thanks

Answer 1

Backward selection approach begins with the full least squares model containing all p predictors(Independent variables) and then iteratively removes the least useful predictor, one-at-a-time.

The steps inlcude are:

• Start with all independent variables in the model.

• Remove the variable with the largest p-value that is, the variable that is the least statistically significant.

• The new (p - 1)-variable model is t, and the variable with the largest p-value is removed.

• Continue until a stopping rule is reached. For instance, we may stop when all remaining variables have a significant p-value defined by some significance threshold.

Thus in this way you will get the key independent variables required for model building/fit regression and there will be no multicollinearity in the dataset as those variables are dropped in this approach.

Hope you will get your answer , in case of any doubt you can ask in comment.