Mathematics
Multiple Regression and the Problem of Multicollinearity
Quick fact
When two predictors are perfectly correlated, it is impossible to calculate separate coefficient estimates—the model literally cannot distinguish their individual effects, and the coefficients become undefined.
Why this is interesting
Ever wondered why adding more variables to your regression model sometimes makes the results less trustworthy? In multiple regression, predictors can secretly sabotage each other when they are too similar—this is the problem of multicollinearity.