What does a correlation model indicate about two random variables?

A correlation model is a statistical tool that assesses the strength and direction of the linear relationship between two random variables. When we say that the correlation between two variables is strong, it means that as one variable changes, the other variable tends to change in a predictable manner. Moreover, the direction of the correlation tells us whether the variables move together (positive correlation) or in opposite directions (negative correlation).

This model does not quantify the absolute difference in means or establish a causal relationship between the variables. Instead, it focuses solely on how closely the data points fit a linear trend. Variations between the variables can be independent or dependent, but a correlation model does not specifically demonstrate independence; it merely describes how changing one variable relates to changes in the other. Therefore, the correct interpretation of a correlation model centers on the strength and direction of the linear relationship it identifies.