ABSTRACT
The term ‘multicollinearity’ refers to a situation in which there is an exact linear relation among two or more of the independent variables. The existence of multicollinearity affects the estimation of the model as well as the interpretation of the results. The inclusion of large number of independent variables in a regression model and in absence of orthogonality or presence of near-linear dependencies among the independent variables leads to the problem of Multicollinearity. So, the problem of multicollinearity arises in a Multiple Linear Regression Model due to strong inter-correlation among the independent variables. When the independent variables are very highly correlated with each other then, the problem of multicollinearity arises. Multicollinearity suggests that, one or more of the independent variables are linearly related in the sample, but that there is no real causal relationship between them.
