What does correlation measure?

Correlation specifically measures the strength and direction of the linear relationship between two variables. It provides insight into how one variable may change in relation to another. For example, if two variables are positively correlated, an increase in one variable would typically correspond with an increase in the other. Conversely, a negative correlation indicates that as one variable increases, the other tends to decrease.

This relationship is quantified using a correlation coefficient, which ranges from -1 to +1. A coefficient close to +1 indicates a strong positive correlation, while a coefficient near -1 signifies a strong negative correlation. A coefficient around 0 suggests no linear relationship between the variables.

In contrast, other options address different aspects of data analysis. The difference between two variables focuses on their individual values rather than their relationship. A cause-and-effect relationship implies a more deterministic connection than correlation indicates, while the average of a set of data pertains to central tendency rather than relational metrics. Therefore, the correct answer reflects the specific role of correlation in statistical analysis.