That is, we can estimate the parameter vector b , but not very precisely. Our problem is a sample phenomenon. The sample may not be rich enough to allow for all the effects we believe to exist. Then we can show where Clearly, the classic case of multicollinearity affects the precision of our estimator. Of course, in the event that x 1 and x 2 have an exact linear relationship the situation is even worse.
All t statistics are significant.
The inclusion of x 5 has sopped up all the variation in y that had been previously explained by x 3 . The correlation between them is .99. We can find the characteristic vectors of x'x. L , where L is a diagonal matrix of the characteristic roots of x'x. If one of the a jl 2 is quite large relative to its characteristic root then we have a problem.