Unless, of course, you use an r^2 that accounts for the number of
covariates you include. That is why I asked about which r^2 you are
interested in. To say that "R-squared is not a statistical concept." is
silly.
Silly, perhaps. But I also think talking about 'which r^2' is a bit
silly. r^2 is a distinct concept: the squared Pearson correlation
between observed and predicted. Marcello is certainly correct in
suggesting that we _could_ speak of a confidence interval around a
correlation coefficient. Strangely enough a significance level cannot
be produced by -correlate-, although it can by -pwcorr-. There is, as
Nick Cox points out, an entire bestiary of R^2-type measures, pseudo-
r^2s, r^2 from a model lacking a constant term, etc. But when we
speak of r^2 arising from linear regression with a constant term
included I don't think there is room for disagreement about "which one".
Of course, what you may observe in this dialogue is the difference
between a real statistician and a nominal statistician. Marcello has
a much stronger claim on the subject than do I. I just use the stuff.
