In PROC HPLOGISTIC, which fit statistic should a data scientist examine to evaluate whether adding a predictor significantly improves the model compared to a nested simpler model?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the Likelihood Ratio Test compares nested models by computing -2*(log likelihood difference) which follows a chi-squared distribution; the Type 3 analysis in PROC HPLOGISTIC reports this for each predictor. A (Pearson chi-square) tests overall model calibration, not the contribution of individual predictors.
Full explanation below image
Full Explanation
B is correct because the Likelihood Ratio Test compares nested models by computing -2*(log likelihood difference) which follows a chi-squared distribution; the Type 3 analysis in PROC HPLOGISTIC reports this for each predictor. A (Pearson chi-square) tests overall model calibration, not the contribution of individual predictors. C (Hosmer-Lemeshow) tests overall calibration across probability deciles, not predictor contribution. D (Nagelkerke R-squared) is a descriptive pseudo R-squared, not an inferential test.