Detecting Multicollinearity

Multicollinearity is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. This often leads to confusing and inconsistent results because individual predictor variables will either appear to have either no correlation or too much correlation with the dependent variable. Multicollinearity can also lead to incorrect coefficient estimates, wide confidence intervals, and biased estimates. It is important to detect multicollinearity in a multiple regression model to reduce the risk of incorrect or misinformed interpretations of the results.