A model's results show increasing explanatory value as additional independent variables are added to the model. Which of the following is the most appropriate statistic?
→ Adjusted R² is specifically designed to evaluate the goodness-of-fit of a regression model while adjusting for the number of predictors. Unlike R², which always increases with more variables, adjusted R² penalizes for adding irrelevant predictors and provides a more accurate measure of model quality.
Why the other options are incorrect:
B: p-values assess significance of individual predictors, not overall model performance.
C: χ² tests are used in categorical data, not regression fit.
D: R² may be misleading when more variables are added — it always increases or stays the same.
Official References:
CompTIA DataX (DY0-001) Official Study Guide – Section 3.2:“Adjusted R² accounts for the number of predictors, making it suitable for comparing models with different numbers of variables.”
Applied Regression Analysis, Chapter 5:“Adjusted R² is used to judge whether adding predictors actually improves the model beyond overfitting.”
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