In result, many pairwise correlations can be viewed together at the same time in one table. Learn more about correlation vs regression analysis with this video by 365 Data ScienceĬorrelation is a more concise (single value) summary of the relationship between two variables than regression. Typically, regression is used when X is fixed. *The X variable can be fixed with correlation, but confidence intervals and statistical tests are no longer appropriate. Prism helps you save time and make more appropriate analysis choices. Correlation is a single statistic, whereas regression produces an entire equation.With correlation, X and Y are typically both random variables*, such as height and weight or blood pressure and heart rate. Regression assumes X is fixed with no error, such as a dose amount or temperature setting.With correlation, the X and Y variables are interchangeable. Regression attempts to establish how X causes Y to change and the results of the analysis will change if X and Y are swapped.It represents the proportion of variation in Y explained by X. The correlation squared (r2 or R2) has special meaning in simple linear regression.When the correlation is positive, the regression slope will be positive.When the correlation (r) is negative, the regression slope (b) will be negative.Both quantify the direction and strength of the relationship between two numeric variables.Simple linear regression relates X to Y through an equation of the form Y = a + bX.

The similarities/differences and advantages/disadvantages of these tools are discussed here along with examples of each.Ĭorrelation quantifies the direction and strength of the relationship between two numeric variables, X and Y, and always lies between -1.0 and 1.0. When investigating the relationship between two or more numeric variables, it is important to know the difference between correlation and regression.

Regress y on x