unlikely to vote Labour and vice versa. Because this overlap is carried
forward to the computation of R^2, R^2 has been upwardly biased.
Thanks, but I'm afraid I still don't follow. If the beta coefficients were
all zero, R^2 would be zero. Further, while the intercorrelations of the
Xs may affect how large R^2 is, I don't see how that causes R^2 to be
"upwardly biased", i.e. just because something causes R^2 to be bigger
doesn't mean that it becomes biased towards a larger value. I'm aware of
various consequences of multicollinearity, e.g. large standard errors,
large confidence intervals, increased likelihood of saying a coefficient
does not differ from zero when it really does. But, I don't remember ever
hearing "upwardly biased R^2" as a problem. But that doesn't mean I
couldn't have missed it! But multicollinearity does not cause regression
coefficients to be biased (wildly variable from one sample to the next,
maybe, but not biased) so I am not sure why it would cause R^2 to be biased.
