The intercept term is now equal to
0.000. This isn't that surprising, since the intercept is still
computed as previously, the square of the grand mean multiplied by N.
However, what is the mean of centered data? The mean of centered data
is always equal to 0. So, the computation is literally (0.000)(100) =
0. The test of significance on the intercept term tests the same thing
as it did previously (i.e., before we centered the data), the null
hypothesis that the mean of the count data is equal to 0. Not
surprisingly, it is 100% not significant (p = 1.000). Again, the
intercept term isn't of interest to us here, because by centering, we
already knew how it would turn out. Notice that the F ratios for the
other terms have not changed simply because we centered.
Where centering data, and interpreting the intercept term really comes
into play, is in regression-style models. In traditional ANOVA-style
models, centering the dependent variable isn't extremely common.