Simple Linear Regression

Summary

Simple and multiple linear regression are both statistical techniques used to model the relationship between a dependent variable and one or more independent variables.

The key difference between them lies in the number of independent variables used in the model.

Usage

Simple linear regression involves a single independent variable to predict the value of a dependent variable. It is based on the assumption that there is a linear relationship between the two variables.

This is represented by the equation $y=B_0+B_1x+ \epsilon$, where $y$ is the dependent variable, $B_1x$ is the independent variable, $B_0$ is the y-intercept, $B_1$ is the slope of the line, and $\epsilon$ is the error term.

Why?

Why use a simple linear regression? Because simple models sometimes are sufficient. Sometimes something more complicated is not necessary.

References

• Simple Linear Regression by New Castle University.