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

\(p \approx \Pr(F_{df_1,df_2} \ge f)\).

## See Also

Reading Tables and Interpreting Significance Tests

Overview of Statistical Testing in Q