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It is useful to consider the second definition to get the idea behind FVU. When trying to predict ''Y'', the most naïve regression function that we can think of is the constant function predicting the mean of ''Y'', i.e., <math>f(x_i)=\bar{y}</math>. It follows that the MSE of this function equals the variance of ''Y''; that is, ''SS<sub>E</sub>'' = ''SS<sub>T</sub>'', and ''SS<sub>R</sub>'' = 0. In this case, no variation in ''Y'' can be accounted for, and the FVU then has its maximum value of 1.
More generally,
==See also==
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