DA0-002 Data Analysis Practice Question
A data analyst is evaluating a multiple regression model with three predictors. The R² value is 0.85. Which of the following is the best interpretation of R²?
⚠ Common exam trap
DA0-002 often tests the misconception that R² is a percentage of correct predictions or a correlation value — candidates confuse goodness-of-fit with accuracy or with Pearson's r.
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
✓
85% of the variance in the outcome is explained by the predictors.
R² (coefficient of determination) measures the proportion of variance in the dependent variable explained by the independent variables in the model. An R² of 0.85 means 85% of the variability in the outcome is accounted for by the three predictors, with the remaining 15% attributable to factors not in the model or random noise. This is a goodness-of-fit measure, not an accuracy percentage or correlation coefficient.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
85% of the variance in the outcome is explained by the predictors.
Why this is correct
R² measures the proportion of total variance in the dependent variable accounted for by the three predictors collectively. A value of 0.85 therefore means 85% of outcome variance is explained, with the remaining 15% attributable to other factors or error.
- ✗
85% of the predicted values are correct.
Why it's wrong here
R² quantifies explained variance in the response variable, not the percentage of predictions that are correct. It is tempting because 0.85 sounds like an accuracy rate, but classification accuracy is a separate metric; R² concerns continuous variance explained by the three predictors.
- ✗
The model has a high bias.
Why it's wrong here
R² measures the proportion of variance in the dependent variable explained by the predictors, not bias. It is tempting because a high R² can accompany underfitting in some contexts, but bias is assessed through residual patterns and validation, not this coefficient of determination.
- ✗
The model has a strong correlation of 0.85.
Why it's wrong here
R² is the squared multiple correlation, so 0.85 means the multiple correlation is about 0.92, not 0.85. It is tempting because R² and r share a scale, but confusing them misstates the relationship strength between predictors and the response.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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