How does correlation generally differ from linear regression?

Correlation and linear regression are both statistical tools used to analyze the relationship between two variables, but they serve distinct purposes. The correct choice highlights that correlation specifically measures the strength and direction of a linear relationship between two variables, providing insight into how closely related they are. It quantifies this relationship into a single correlation coefficient, which can range from -1 to 1.

On the other hand, linear regression goes a step further by modeling the relationship in order to make predictions. It does this by fitting a line (the regression line) through the data points in a way that minimizes the distance between the observed values and the predictions made by the linear model. This means that regression not only identifies relationships but also helps in forecasting or estimating the value of the dependent variable based on the independent variable.

In summary, while correlation focuses on the strength of the relationship, regression provides a way to model and predict outcomes based on that relationship. This distinction is crucial in understanding how to appropriately use each statistical concept in data analysis.