Multiple Regression Diagnostics: Although the assumptions of the multiple regression model are met, one can find some other problems resulting from outliers and multicollinearity. Today we will learn how to obtain several diagnostic statistics for outliers and multicollinearity.
1. Outliers: outliers are cases with extreme values that differ substantially from the rest of your sample, which are also known as influential cases.
2. Multicollinearity: multicollinearity refers to the case where two or more predictors are highly correlated, so that it is difficult to isolate their individual effects on the dependent variable.