In which scenario would you use linear regression instead of correlation?

Linear regression is specifically designed to model the relationship between two variables, allowing one to be used as a predictor for the other. In this context, it provides a way to estimate the value of a dependent variable based on the known value of an independent variable. When conducting linear regression, you derive a line of best fit that describes how changes in the independent variable impact the dependent variable.

This predictive capability is essential in various fields, such as finance, healthcare, and marketing, where understanding how one variable influences another can guide decision-making. For example, if you want to predict sales based on advertising expenditure, linear regression allows you to quantify that relationship and make informed projections.

While correlation measures the strength and direction of a relationship between two variables, it does not imply causation. Correlation alone cannot be used to predict outcomes like linear regression can. The other options involve assessing relationships or calculations that do not require the predictive aspect inherent to linear regression.