DA0-002 Data Analysis Practice Question
An analyst is performing a linear regression and obtains an R-squared value of 0.85. Which of the following is the best interpretation?
⚠ Common exam trap
DA0-002 often tests the interpretation of R-squared, and candidates frequently confuse it with correlation, causation, or the proportion of data points on the line.
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
✓
The model explains 85% of the variability in the dependent variable.
R-squared (R²) is the coefficient of determination, representing the proportion of variance in the dependent variable that is predictable from the independent variable(s). An R² of 0.85 means that 85% of the variability in the dependent variable is explained by the regression model, indicating a strong fit. This is the standard statistical interpretation.
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 residuals are zero.
Why it's wrong here
R-squared quantifies explained variance in the response variable, not the proportion of residuals equal to zero; residuals are rarely exactly zero even in strong fits. It is tempting because small residuals accompany good fit, but the statistic concerns variance, not residual counts.
- ✗
85% of the data points lie on the regression line.
Why it's wrong here
R-squared measures the proportion of variance in the dependent variable explained by the predictors, not the share of observations lying exactly on the fitted line; most points deviate slightly even with strong fit. It is tempting because a high value implies tight clustering around the line.
- ✗
There is an 85% chance that the relationship is causal.
Why it's wrong here
R-squared measures the proportion of variance in the dependent variable explained by the model, not the probability of causation; correlation never establishes causality without controlled design. It is tempting because a high value suggests a strong relationship, but causal claims require experimental or quasi-experimental evidence.
- ✓
The model explains 85% of the variability in the dependent variable.
Why this is correct
R-squared measures the proportion of variance in the dependent variable accounted for by the regression model. A value of 0.85 therefore means the predictors explain 85% of that variability, leaving 15% unexplained by the model.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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