Multiple linear regressions represents a generalization, to more than a single explanatory variable, of the simple linear regression procedure. The adjective “multiple” indicates that at least two explanatory variables are involved in the modeling exercise. Software packages frequently offer automatic methods of selecting variables for a final regression model from a list of candidate variables. There are three typical approaches: forward selection, backward elimination and stepwise regression. The stepwise regression method of variable selection combines elements of both forward selection and backward elimination. Multiple linear regressions are used to assess the relationship between a set of explanatory variables and a continuous-response variable. The models used in analysis of variance (ANOVA) are equivalent to those used in multiple linear regression. By using dummy variables to appropriately code the levels of the factors in an ANOVA design, the model for the design can be put in the form of a multiple linear regression model.