Now consider the correlations among the three predictors. They are all exactly 0. Other effects can be computed in the same way. The multiple R2 is just the sum of the simple r2's and equals 0.8188. This is the proportion of variance explained.
Every cell has two subjects except the last which has three.
The correlation between A and Y may be due, in part, to the correlaton between B and Y. One solution is to consider only the unique contribution of each variable. The sum of squares for A is just the difference between the two models.