Correlation measures the strength of the linear relationship between two variables, while regression quantifies the relationship and predicts the dependent variable based on the independent variable.

Correlation measures the strength and direction of a relationship between two variables. It tells you how closely the variables move together but does not imply causation; for example, if one variable increases, the other may also increase or decrease.

Regression, on the other hand, not only assesses the relationship between variables but also predicts the value of one variable based on another. It helps establish a mathematical equation that describes how changes in the independent variable(s) can affect the dependent variable, indicating potential cause-and-effect relationships. In summary, correlation shows the relationship, while regression explains and predicts that relationship.