Having many variables in #regression in #dataanalysis is ideal case of #econometrics but when you have too many #independent variables which are expected to be #collinear or #correlated too, the you have to reduce the number of the variables from the model so that the #biasness from #multicollinearity is removed. This tutorial provides you demonstration on one of the method which forms subgroups of variables based on their similarity in explaining the dependent variable. Principal Axis Factoring #PAF to make #indices of similar variables and #converge them into multiple indices if required using #KMO , #bartlet test and #factor #loading.

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