<Dd> F = (RSS 1 − RSS 2 p 2 − p 1) (RSS 2 n − p 2), (\ displaystyle F = (\ frac (\ left ((\ frac ((\ text (RSS)) _ (1) - (\ text (RSS)) _ (2)) (p_ (2) - p_ (1))) \ right)) (\ left ((\ frac ((\ text (RSS)) _ (2)) (n - p_ (2))) \ right))),) </Dd> <P> where RSS is the residual sum of squares of model i . If the regression model has been calculated with weights, then replace RSS with χ, the weighted sum of squared residuals . Under the null hypothesis that model 2 does not provide a significantly better fit than model 1, F will have an F distribution, with (p − p, n − p) degrees of freedom . The null hypothesis is rejected if the F calculated from the data is greater than the critical value of the F - distribution for some desired false - rejection probability (e.g. 0.05). The F - test is a Wald test . </P>

The f value is calculated as which of the following