Multiple correlation involves analyzing the relationship between multiple independent variables and a single dependent variable. It provides information about how well a set of independent variables predict the dependent variable. In contrast, regression involves using multiple independent variables to predict a single dependent variable. By performing regression analysis, we can understand the impact of each independent variable on the dependent variable and predict the outcome based on those variables. In simpler terms, multiple correlation assesses the overall relationship between multiple independent variables and a dependent variable, while regression specifically looks at how each independent variable contributes to predicting the dependent variable.
