The article discusses multiple regression, a statistical method that helps predict outcomes based on multiple factors. It extends simple linear regression by allowing for more than one independent variable. The model can describe not only straight-line relationships but also curved ones using techniques like polynomial regression. The article explains how to estimate coefficients, calculate error variance, and make inferences about the model parameters. It also touches on correlations that show how variables are related. To avoid using too many independent variables, researchers can select a subset that best explains the data.