Collinearity refers to linear relationships between two X variables. Multicollinearity encompasses
linear relationships between more than two X variables. Multiple regression is impossible in the
presence of perfect collinearity or multicollinearity. If X1 and X2 have no independent variation, we
cannot estimate the effects of X1 adjusting for X2 or vice versa. One of the variables must be
dropped. This is no loss, since a perfect relationship implies perfect redundancy. Perfect
multicollinearity is, however, rarely practice problem. Strong (not perfect) multicollinearity, which
permits estimation but makes it less precise, is more common. When the multicollinearity is present,
the interpretation of...