Multiple correlation studies the relationship between one dependent variable and two or more independent variables. It shows how strongly a group of predictors together explain changes in the outcome. The multiple correlation coefficient (R) measures this combined relationship, ranging from 0 to 1. For example, sales (Y) may depend on advertising (X₁) and price (X₂). Common options include using regression models and correlation matrices. Important formulas include the multiple R, R², and regression equation Y = a + b₁X₁ + b₂X₂. Problems usually involve calculating R, interpreting R², and predicting Y using given data.