Could you say that there is a inverse relationship between SEE and R2?

Or are there situtations where the SEE and R2 are not related to eachother. If so, could you provide an example?

I feel like if SEE increases, variation of residuals increases as well meaning the line would not be as good of a fit for the y variable and thus have a lower R2, but I am not sure if there are examples that would make this into a non definitive relationship/instance where this relationship is not true at all. Thanks in advance.

…Since SEE^2 is the (estimated) variance of the errors of prediction and Var(Y) is the total variation in the DV…just use a small amount of algebra to rearrange the clunky formula in the book to get the one above…