One key advantage of including multiple predictors in the regression equation is that it can detect the extent to which two or more predictor variables interact. In the multiple regression, it can define situations in which one variable or the predictor variable is used to predict another variable which the criterion variable. While a single variable can be used to make accurate predictions, many behaviors are often too complex to be represented by a simple linear equation. That is often the changes in a single predictor variable do not allow to accurately predict changes in a criterion variable. Predictions of many behaviors improve when it considers more information in predictor variable. Multiple regression is used when using multiple predictor variables to predict changes in a criterion variable.
