What is the difference between correlation and regression?

• A. Correlation measures association strength; regression models predict the dependent variable from one or more predictors.
• B. They are the same concept.
• C. Correlation implies causation; regression does not.
• D. Regression measures means differences between groups.

Answer: (A)

The key idea is that correlation and regression serve different purposes in understanding relationships. Correlation measures how strongly two variables are related and in what direction, giving a single index that is the same no matter which variable you treat as X or Y. It describes association without implying that one variable is being predicted from the other or that a causal link exists. In contrast, regression builds a predictive model that uses one or more predictor variables to estimate a dependent variable. It yields an equation that tells you how much the outcome is expected to change as the predictors change, and it designates which variable is the outcome to be predicted. Because of this, correlation is about the strength and direction of a relationship, while regression is about prediction and estimating the effect of predictors on an outcome.