DA0-002 Data Analysis Practice Question
A data scientist builds a simple linear regression model to predict house prices based on square footage. The model yields an R-squared value of 0.85. Which statement accurately interprets this result?
⚠ Common exam trap
The trap is treating R² as a probability or a count of points on the line — candidates often misread it as '85% chance' or '85% of points fit exactly,' when it strictly measures explained variance.
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 house prices
R-squared (the coefficient of determination) measures the proportion of variance in the dependent variable explained by the independent variable(s). An R² of 0.85 means 85% of the variability in house prices is accounted for by the square footage in this linear model. It is a goodness-of-fit measure, not a probability, slope, or count of points on the line.
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 slope of the regression line is 0.85
Why it's wrong here
R-squared is the coefficient of determination, quantifying variance explained, not the regression slope. The slope is a separate coefficient derived from the data. Confusing the two is tempting because both are numeric outputs of the same model, but they describe entirely different properties.
- ✗
85% of the data points lie exactly on the regression line
Why it's wrong here
R-squared measures the proportion of variance in house prices explained by square footage, not the fraction of points sitting on the line. It is tempting because 0.85 sounds like a percentage of correct predictions, but residuals exist for nearly every observation; only a perfect fit would place all points on the line.
- ✓
The model explains 85% of the variability in house prices
Why this is correct
R-squared measures the proportion of variance in the dependent variable explained by the model. A value of 0.85 means square footage accounts for 85% of house price variability, directly satisfying the stem's constraint of interpreting the reported R-squared value.
- ✗
There is a 85% chance that square footage causes higher prices
Why it's wrong here
R-squared measures the proportion of variance in house prices explained by square footage, not a probability of causation. It is tempting because a high value suggests a strong relationship, but it quantifies goodness of fit, not causal likelihood; correlation never establishes causation regardless of magnitude.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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