SIMPLE LINEAR REGRESSION AND LINEAR REGRESSION MODELS
Simple linear regression: a single independent variable is used to predict the value of a dependent variable. Multiple linear regression: two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables.
Linear Regression Models: In statistics, linear regression models states the relationship between a dependent variable and one or more explanatory variables using a linear function. If two or more explanatory variables have a linear relationship with the dependent variable, the regression is called a multiple linear regression. Multiple regressions, on the other hand, is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.