Multicollinearity is one of several problems confronting researchers using regression analysis. This paper examines the regression model when the assumption of independence among Ute independent variables is violated. The basic properties of the least squares approach are examined, the concept of multicollinearity and its consequences on the least squares estimators are explained. The detection of multicollinearity and alternatives for handling the problem are then discussed. The alternative approaches evaluated are variable deletion, restrictions on the parameters, ridge regression and Bayesian estimation.