Multicollinearity in multiple linear regression:

A. Is good, because it means that every pair of variables being considered exhibits a linear relationship, simplifying the analysis.

B. Is good, because it means the regression lines associated with each independent variable are parallel to one another.

C. Is bad, because when we do MLR, we like to assume that all of the independent variables are also independent of each other.

D. Is bad, because it means that there is another independent variable contributing significantly to the variation in Y-values that we haven't yet considered.