DA0-002 Data Analysis Practice Question
A data analyst is examining the relationship between two continuous variables: temperature and ice cream sales. The analyst wants to quantify the strength and direction of their linear association. Which statistical measure should the analyst use?
⚠ Common exam trap
The trap here is selecting covariance because it also measures linear relationship, but covariance lacks standardization and does not convey strength on a fixed scale.
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
✓
Pearson correlation coefficient
Pearson correlation coefficient is the standard measure for quantifying the strength and direction of a linear relationship between two continuous variables. It is scale-independent, ranging from -1 to 1, and directly addresses the analyst's goal. Other options either measure different types of association or are not suitable for continuous data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Covariance
Why it's wrong here
Covariance indicates the direction of a linear relationship but its magnitude depends on the scales of the variables, making it difficult to interpret strength. Unlike Pearson correlation, covariance is not standardized. The analyst wants a measure of both strength and direction, so correlation is preferred over covariance.
- ✗
Spearman rank correlation
Why it's wrong here
Spearman rank correlation assesses monotonic relationships, not necessarily linear ones, and is used for ordinal data or when linearity is not assumed. While it could be applied here, the analyst specifically wants to quantify linear association, so Pearson is more precise. Spearman would provide a less direct measure of the linear relationship between temperature and sales.
- ✓
Pearson correlation coefficient
Why this is correct
The Pearson correlation coefficient measures the strength and direction of a linear relationship between two continuous variables. Temperature and ice cream sales are both continuous, and the analyst seeks a linear association, making Pearson correlation the appropriate measure. It ranges from -1 to 1, indicating perfect negative to perfect positive linear relationships.
- ✗
Chi-square test of independence
Why it's wrong here
The chi-square test is used for categorical variables to determine if they are independent. Temperature and ice cream sales are continuous, so chi-square is inappropriate. Applying it would require arbitrary binning of continuous data, which reduces statistical power and does not directly measure linear association.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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