Published on February 19, 2020 by Rebecca Bevans. Revised on October 26, 2020.
Regression models describe the relationship between variables by fitting a line to the observed data. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change.
Simple linear regression is used to estimate the relationship between two quantitative variables. You can use simple linear regression when you want to know:
How strong the relationship is between two variables (e.g. the relationship between rainfall and soil erosion).
The value of the dependent variable at a certain value of the independent variable (e.g. the amount of soil erosion at a certain level of rainfall).
ExampleYou are a social researcher interested in the relationship between income and happiness. You survey 500 people whose incomes range from $15k to $75k and ask them to rank their happiness on a scale from 1 to 10.
Your independent variable (income) and dependent variable (happiness) are both quantitative, so you can do a regression analysis to see if there is a linear relationship between them.