A linear regression model describes the relationship between a response (output) variable and a predictor (input) variable. In a linear regression model, the response variable is expressed as an equation that is linear in the regression coefficient of the predictor variable.
In a multiple linear regression model, the response variable depends on more than one predictor variable, and is expressed as the sum of a constant term and additional terms. Each additional term is the product of a regression coefficient and a function involving one or more predictor variables. A multivariate linear regression model includes more than one response variable.
For more information, see What Is a Linear Regression Model?
- Multiple Linear Regression
Linear regression with multiple predictor variables
- Stepwise Regression
Variable selection in linear regression model using stepwise regression
- Multivariate Linear Regression
Linear regression with a multivariate response variable
Ridge regression, lasso, and elastic nets for linear models
- Mixed Effects
Linear mixed-effects models