DA0-002 Data Analysis Practice Question
A data analyst is performing a multiple linear regression with three predictors. The model output shows an R-squared of 0.85 and an adjusted R-squared of 0.80. Which of the following is the best interpretation of the difference between these two values?
⚠ Common exam trap
DA0-002 often tests whether candidates understand that adjusted R-squared penalizes complexity, so the trap is picking 'overfitting' (A) when the correct interpretation is simply that some predictors lack meaningful contribution.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One or more predictors may not be contributing meaningfully
R-squared (0.85) measures how much variance the model explains, while adjusted R-squared (0.80) penalizes for the number of predictors and only increases if a new predictor improves the model more than chance would predict. A notable drop between the two (0.05) suggests that at least one predictor is not contributing meaningfully and may be adding noise rather than explanatory power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is overfitted, so all predictors should be removed
Why it's wrong here
A 0.05 gap indicates mild shrinkage from adding three predictors, not overfitting requiring removal of all predictors; adjusted R-squared remains high at 0.80. Removing predictors would discard genuine explanatory power. Overfitting is tempting because the gap does signal some penalty, but the correct response is evaluating individual predictor significance.
- ✗
The model has high multicollinearity
Why it's wrong here
Multicollinearity is detected via variance inflation factors or correlation matrices among predictors, not the R-squared versus adjusted R-squared gap. That difference quantifies the penalty for predictor count. Analysts confuse the two because both concern multiple predictors, but VIF measures predictor interdependence, not model fit shrinkage.
- ✗
The residuals are not normally distributed
Why it's wrong here
R-squared and adjusted R-squared do not assess normality of residuals.
- ✓
One or more predictors may not be contributing meaningfully
Why this is correct
Adjusted R-squared penalises each added predictor, so a drop from 0.85 to 0.80 signals that some of the three predictors add little explanatory power relative to the degrees of freedom they consume. This satisfies the stem's request to interpret the gap between the two metrics.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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