Multicollinearity may be a possible cause in case of study with two or more explanatory variables. In the presence of multicollinearity, the design matrix becomes nearly singular and hence X and the corresponding XX are not of full rank. In this situation, the Ordinary Least Square (OLS)...

The paper begins with a discussion on the historical facts and evidence about the innovation of least square technique as a method of estimating (single equation) multiple regression models from the time of Euler to the present. It is intriguing to notice that this method has been in frequent...

This paper axiomatically characterizes Generalized Inverse Regression (GIR) only to combat multicollinearity. Generalized Inverse (GI) estimator is a better alternative to Ordinary Least Square (OLS) estimator in case of ill-conditioning. After discussing Moore-Penrose and Rao’s generalized...