DA0-002 Data Analysis Practice Question
In simple linear regression, the coefficient of determination R² measures:
⚠ Common exam trap
The trap is confusing R² with Pearson's r — R² measures explained variance (0 to 1) while r measures strength and direction (−1 to +1), and the exam offers both as plausible-sounding options.
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 proportion of variance in the dependent variable explained by the independent variable
R², the coefficient of determination, quantifies the proportion of variance in the dependent variable that is explained by the independent variable(s) in the regression model. It ranges from 0 to 1, where 1 means the model explains all variance and 0 means it explains none.
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 probability that the slope is zero
Why it's wrong here
R² is a goodness-of-fit proportion, not a probability, and it does not test whether the slope equals zero; that inference comes from the slope's t-statistic and p-value. It is tempting because a near-zero R² often accompanies a non-significant slope, but the two quantities are distinct and computed differently.
- ✗
The slope of the regression line
Why it's wrong here
The slope is the estimated change in the dependent variable per unit change in the predictor, a separate regression coefficient; R² instead reports the proportion of total variance explained. It is tempting because both derive from the same fitted line, but the slope's magnitude depends on measurement units, whereas R² is unitless.
- ✓
The proportion of variance in the dependent variable explained by the independent variable
Why this is correct
R² quantifies the share of total variance in the dependent variable accounted for by the fitted regression on the independent variable, expressed as a proportion between 0 and 1. It therefore directly satisfies the stem's requirement to measure explained variance rather than correlation strength or slope.
- ✗
The strength and direction of the linear relationship
Why it's wrong here
R² quantifies the proportion of variance in the dependent variable explained by the regression model, not the direction or strength of association; Pearson's r captures both strength and direction. It is tempting because R² and r are numerically linked in simple regression, yet R² is always non-negative and so cannot express direction.
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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.