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The test statistic is:

 f = \frac{\sum^n_{i=1} w_i(\hat{y}_i - \bar{y})^2 / df_1}{\sum^n_{i=1} w_i(y_i - \hat{y}_i )^2 / df_2}


y_i is the ith of n observed values of a numeric variable,
\hat{y}_i is a value fitted by weighted least squares,
df_1 = k,
k is the number of independent variables in the weighted least squares (excluding the constant),
df_2 = \sum^n_{i=1}w_i - k - 1,
w_i is the Calibrated Weight, and
p \approx \Pr(F_{df_1,df_2} \ge f).

See also