Homogeneity of variance (homoscedasticity): the size of the error in our prediction doesn’t change significantly across the values of the independent variable.
Simple linear regression is a parametric test, meaning that it makes certain assumptions about the data.
Frequently asked questions about simple linear regression.
Can you predict values outside the range of your data?.
How to perform a simple linear regression.
Assumptions of simple linear regression.
If you have more than one independent variable, use multiple linear regression instead. 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. 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. Simple linear regression exampleYou are a social researcher interested in the relationship between income and happiness. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression models describe the relationship between variables by fitting a line to the observed data.
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).
How strong the relationship is between two variables (e.g., the relationship between rainfall and soil erosion).
You can use simple linear regression when you want to know: Simple linear regression is used to estimate the relationship between two quantitative variables. Simple Linear Regression | An Easy Introduction & Examples
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