CompTIA Data+ (DA0-002)Data AnalysisMedium
A data analyst is investigating the relationship between advertising spend and sales revenue. They calculate a Pearson correlation coefficient of +0.85 between the two variables. What can be concluded from this correlation coefficient?
- AIncreasing advertising spend causes an increase in sales revenue.
- BFor every $1 increase in advertising spend, sales revenue increases by $0.85.
- C85% of the variation in sales revenue is explained by advertising spend.
- DThere is a strong positive linear relationship between advertising spend and sales revenue.
Show answer & explanationAnswer & explanation
Correct answer: D. There is a strong positive linear relationship between advertising spend and sales revenue.
A Pearson correlation coefficient of +0.85 indicates a strong positive linear relationship between the two variables. The positive sign means they move in the same direction, and 0.85 is close to 1, signifying a strong relationship.
Why the other options are wrong
- A. Correlation does not imply causation. While they are related, we cannot definitively say one causes the other based solely on correlation.
- B. This is an interpretation of a regression slope, not a correlation coefficient. Correlation measures the strength and direction, not the magnitude of change.
- C. This interpretation refers to the coefficient of determination (R-squared), which is the square of the correlation coefficient (0.85^2 ≈ 0.7225 or 72.25%).
Pearson Correlation Coefficient (r)
A measure of the linear correlation between two sets of data.
- Ranges from -1 to +1.
- Sign indicates direction (positive/negative).
- Magnitude indicates strength (closer to ±1 is stronger).
- Does not imply causation.
Memory trick: CORRELATION: Does it go together, and how TIGHTLY?