DA0-002 Data Analysis Practice Question
A data analyst wants to understand the relationship between advertising spend and sales revenue. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following is the best interpretation?
⚠ Common exam trap
A common mix-up: candidates confuse the correlation coefficient r with the coefficient of determination r², causing candidates to select the '85% of variation' answer.
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
✓
There is a strong positive linear relationship between advertising spend and sales.
A Pearson correlation coefficient of 0.85 indicates a strong positive linear relationship between the two variables. The value is close to +1, meaning as advertising spend increases, sales revenue tends to increase in a linear fashion. Correlation measures the strength and direction of a linear association, not causation or predictive proportion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
There is a strong positive linear relationship between advertising spend and sales.
Why this is correct
A coefficient of 0.85 sits near the top of the -1 to +1 range, and its positive sign confirms that as advertising spend rises, sales revenue tends to rise too. The magnitude indicates a strong, near-linear association, satisfying the stem's request to interpret the calculated value.
- ✗
85% of the variation in sales is explained by advertising spend.
Why it's wrong here
Squaring the coefficient gives the proportion of variance shared, so 0.85 yields 0.7225, meaning roughly 72% of variation is explained, not 85%. It tempts because the coefficient itself is 0.85, inviting direct percentage reading, but that value measures linear association strength, not explained variance.
- ✗
Increasing advertising spend by $1 will increase sales by $0.85.
Why it's wrong here
Correlation is standardised and unitless, so it cannot express a dollar change in sales per dollar of advertising; that requires the regression slope. It tempts because 0.85 appears numerically, but the coefficient only quantifies direction and strength of linear association, not magnitude of effect.
- ✗
There is a strong negative linear relationship between advertising spend and sales.
Why it's wrong here
A coefficient of +0.85 is positive, so spend and sales rise together; the sign contradicts the stated direction. Negative correlation would be the correct reading only if the value were around -0.85, indicating one variable falls as the other rises.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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