A suggested method of detecting multicollinearity in multiple regression models

Abstract EN

In literature, several methods suggested for the detection of multicollinearity in multiple regression models, and one of the multicollinearity problems solutions is to omit the explanatory variables in the model, which cause the multicollinearity.

In this paper, we concentrated on the extra sum of squares method as a suggested method that can be used for detecting multicollinearity.

The method of extra sum of squares is applied to real data on the annually surveys about smoking were conducted by the American Federal Trade Commission (FTC).

In this data, we detected multicollinearity, then we solved this problem by using the ridge regression and we got the new estimates of the new model without omitting any of the explanatory variables.