Standard Error of Regression

What is the Standard Error of the Regression (SER)?

The Standard Error of the Regression expresses the degree of uncertainty in the accuracy of the dependent variable’s projected values. It conveniently tells you how far off the regression model is on average by utilising the response variable’s units. It is also called the SE of the estimate. Graphically, the relationship is stronger when the actual x,y data points lie closer to the regression line (errors are smaller).

Example

SER = (SQRT(1 minus adjusted-R-squared)) x STDEV. S(Y)

Why is SER important?

The SER is an absolute measure of how far the data points typically deviate from the regression line.