Dear Jordan,

I got hook by your request so I've tried to implement it. It will be in version 1.5.2, which has also some bug fixes, due soon (end of the week). The small change I made can, I think, solve your case. The Anova table in GLM now shows the total sum of squares and the error sum of squares for all model, including the intercept-only model. I did not implement the SS corrected and not corrected as SPSS does because I think it is just confusing for the general users. Nontheless, I think you can use the new info for your reasoning with the students.

One can now show what is the error SS if one only use the mean of y to predict it, thus one uses an intercept-only model

Then one shows that by adding a variable as predictor, the error SS decreases whereas the total SS stays the same

From there, one can show all the coefficients (R^2, peta^2 and F-tests) with a model comparison approach.

If you really wants to show how the intercept compares with the null-model, than you can still to that by showing the error SS of a model with no intercept and no predictor. You do that in GLM by deflaging the option "Fixed Intercept" in the "Model" option panel. This would estimate a model were the predicted values are all zero.

The Error SS is the "uncorrected" SS that produces SPSS.

I hope it is usefull

mc