The test statistic is:
\[\begin{align} f = \frac{\sum^n_{i=1} w_i(\hat{y}_i - \bar{y})^2 / df_1}{\sum^n_{i=1} w_i(y_i - \hat{y}_i )^2 / df_2}\end{align}\]
where:
\(y_i\) is the \(i\)th 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
\(p \approx \Pr(F_{df_1,df_2} \ge f)\).