What is the difference between correlation and regression?
1 Answer
Both tell you something about the relationship between variables, but there are subtle differences between the two (see explanation).
Explanation:
Correlation calculates the degree to which two variables are associated to each other. It gives you an answer to, "How well are these two variables related to one another?."
A correlation coefficient ranges from -1 to 1. A correlation coefficient of zero indicates that the two variables are not related in any way, a negative value indicates a negative relationship and a positive value a positive relationship. When measuring for correlation, you would sample randomly the independent and the dependent variables from a population. Correlation makes no assumptions about the relationship between variables. Testing for correlation is essentially testing that your variables are independent.
With regression analysis, one can determine the relationship between a dependent and independent variable using a statistical model. Regression analysis determines the effect of one variable on another. You select values for the independent variable in regression analysis.
The result is a regression equation, which gives you a slope and an intercept and is the average relationship between variables. Regression analysis can be used to predict the dependent variable in a new population or sample. Regression assumes that the dependent variable depends on the independent variable. Regression can also examine multiple independent variables at the same time.
To quickly summarize:
Correlation
- Strength of association between variables
- Two variables
- Variables assumed to be random
Regression
- Assign one variable to be the dependent variable
- Can have multiple independent variables
- Predicts one variable based on the other variable(s)
- Result is an equation
