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11-19-2012, 07:11 AM
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Originally Posted by Czech Your Math View Post
I haven't calculated cross-correlations between the variables, but would expect all 4 to have substantial correlations to one another (including negative correlations with Xg), because the changes in each variable are relatively contemporaneous (all of them tended to occur in the 90s... after the 80s and relatively constant after 90s).
The main reason it may be important is that if the correlation between 2 regressors is very high, the coefficient on those 2 variables will be very sensitive to the use of the other variables included in the regression. In English: when 2 variables are closely related, the associations suggested by OLS could be strange or at least misleading.

Originally Posted by Czech Your Math View Post
I was trying to include an intercept [using LINEST in Excel with paramaters (Y range, X ranges, false, true) ], but the intercept calculated as zero. I'll have to check that again, perhaps the false should be a true (but I thought it said that parameter was B = 0, so I entered that as false).
I'm not familiar with LINEST but that 3rd argument should be TRUE if you want to look at R-square.

Originally Posted by Czech Your Math View Post
Yes, I thought about calculating differences first, as I did in an unrelated model. Do you think % (ratio) differences would be better than raw differences?
I don't have a good sense of which one is better (raw differences vs % differences). I think you want a Y that can easily be interpreted, do you want "change in adjusted scoring for the top players" or "% change in adjusted scoring for the top players". Seems to me like raw differences might be better here.

barneyg is offline   Reply With Quote