1. The more the merrier, but the fewer the better!
Often, it’s difficult to determine the impact of an individual influencer on the response variable when multiple influencing factors have more or less the same influence. Let’s streamline this with a realistic example. Say for instance, we want to examine a child’s weight based upon various influencing factors including child’s height and age. It becomes evident that as children grow older, they get taller! Hence, both height as well as age are highly correlated in determining child’s weight. So, this case study has indeed a multicollinearity problem!
