When attempting to communicate how much one variable changes on average as a function of the other, which of the following measures is most appropriate?

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BlackTom AI · Accepted Answer

Here we examine what each measure tells us about the relationship between two variables.
Option 1: Simple Linear Regression Coefficient. This coefficient represents the slope of the regression line, i.e., the average change in the dependent variable for a one-unit change in the independent variable. It directly communicates how much one variable changes on average as the other varies, which matches the question's goal.
Option 2: Correlation Coefficient. This statistic describes the strength and direction of a linear association between two variables, but it is unitless and standardized. It does not quantify the actual amount of change in one variable per unit change in the other, so it does not answer the question about average change magnitude.
Option 3: Covariance. Covariance measures the joint variability of the two variables and reflects direction of association, but its scale depends on the variables' units. It does not provide a standardized rate of change, making it less informative for stating how much one variable changes on average with respect to the other.
In sum, the slope from a simple linear regression captures the requested notion of average change per unit change, whereas correlation describes strength and direction in a standardized way and covariance describes joint variability without conveying a unit-specific rate of change.

FA25_QTM_110_1 Quiz #2: Correlation and Causation

When attempting to communicate how much one variable changes on average as a function of the other, which of the following measures is most appropriate?

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