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?
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
Adjusted R-squared penalizes for adding predictors that do not improve the model significantly; a gap suggests some predictors may be irrelevant or the sample size is small.
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
Adjusted R-squared is still high, indicating good fit; overfitting might be an issue but not necessarily requiring removal of all predictors.
- ✗
The model has high multicollinearity
Why it's wrong here
Multicollinearity affects coefficient estimates, not directly R-squared vs adjusted R-squared.
- ✗
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
The drop from R-squared to adjusted R-squared indicates that some predictors reduce model efficiency.
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