# What is the difference between linear regression and multiple regression?

In statistics, linear regression models 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 regression, on the other hand, is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.

Regression analysis is a common way to discover a relationship between dependent and explanatory variables. However, this statistical relationship does not mean that the explanatory variables cause the dependent variable; it rather speaks of some significant association in the data. Linear regression attempts to draw a line that comes closest to the data by finding the slope and intercept that define the line and minimize regression errors. However, many relationships in data do not follow a straight line, so statisticians