DA0-002 Data Analysis Practice Question
In a regression analysis, the coefficient of determination (R²) is 0.85. How should this value be interpreted?
⚠ Common exam trap
DA0-002 often tests the misconception that R² represents the percentage of points on the line or the slope — candidates must remember it is the proportion of variance explained.
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 dependent variable is explained by the model
R², the coefficient of determination, measures the proportion of variance in the dependent variable that is explained by the independent variables in the regression model. An R² of 0.85 means 85% of the variance in the dependent variable is accounted for by the model, indicating a strong fit.
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 data points lie on the regression line
Why it's wrong here
R² quantifies explained variance, not the proportion of observations falling exactly on the fitted line; residuals mean most points lie off it. It is tempting because a high R² visually suggests a tight fit, but it would describe the line only if every residual were zero, which R² never asserts.
- ✗
The slope of the regression line is 0.85
Why it's wrong here
The slope is a separate regression coefficient expressing change in the dependent variable per unit change in the predictor; R² is a unitless goodness-of-fit measure. It is tempting because both are numeric outputs of the same model, but the slope would be the answer when the question asks about the rate of change.
- ✓
85% of the variance in the dependent variable is explained by the model
Why this is correct
R² measures the proportion of variance in the dependent variable accounted for by the regression model. A value of 0.85 means the model explains 85% of that variance, leaving 15% attributable to other factors or random error.
- ✗
85% of the independent variables are significant
Why it's wrong here
R² measures the proportion of variance in the dependent variable explained by the model, not the significance of independent variables; significance is tested via p-values or t-statistics per predictor. It is tempting because both appear in regression output, but it would be the right interpretation only when reading individual coefficient significance tests.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.