The main difference is that linear regression involves a single independent variable, while multiple regression involves two or more independent variables.
Linear regression and multiple regression are both statistical methods used to model the relationship between a dependent variable and one or more independent variables, but they differ in terms of the number of independent variables involved.
Linear Regression:
Number of Variables: In linear regression, there is one dependent variable (the variable you are trying to predict) and one independent variable (the variable used for making predictions).
Equation: The equation for a simple linear regression is of the form
y=mx+b, where
y is the dependent variable,
x is the independent variable,
m is the slope, and
b is the intercept.
Multiple Regression:
Number of Variables: In multiple regression, there is still one dependent variable, but there are two or more independent variables.
Equation: The equation for multiple regression is an extension of the linear regression equation are the coefficients.