The chapter discusses multiple and polynomial regression, which are methods used to estimate relationships between multiple variables. It explains how to validate the model by examining the overall regression equation, the importance of predictor variables, and how to select the best predictors. The chapter also covers multicomponent analysis using multiple linear regression. Various alternative regression procedures are described for analyzing data with highly correlated predictor variables, such as principal component regression, partial least squares regression, and ridge regression.