A data scientist fits a multiple linear regression model with an intercept and six predictor variables (p = 6) to a sample of n = 80 observations. The model's coefficient of determination is R² = 0.37.
Using the classical F-test for overall model significance (Hâ‚€: all slope coefficients = 0), what is the value of the F statistic that should be reported?
Because this value greatly exceeds typical critical values for the F(6, 73) distribution at α = 0.05 (≈ 2.20), the null hypothesis would be rejected.
The distractor values reflect common errors: computing R² / (1 - R²) alone (≈0.59), using (n - p - 1) / p alone (≈12.2), or omitting the intercept when calculating degrees of freedom (≈37).
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What does the coefficient of determination (R²) indicate in this context?