Research Topics:

Data Analysis | Statistical Analysis

Content Type:

Glossary

Error Sum Of Squares Definition

The variation not explained by the regression.

The error sum of squares, or ESS, is the sum of the squared differences between the observed and predicted values of a statistical model. The variation in the dependent variable is not explained by the regression, or independent variable, in the model. This is used in marketing research to determine the effectiveness of predictive models. A lower ESS a significant portion of the variability in the data. Conversely, a higher ESS may need adjustments or more relevant predictors.

Who relies on the error sum of squares (ESS)?

Researchers, analysts and marketing professionals use error sum of squares calculations to analyze the goodness of fit – a model that describes how well a sample fits a set of observations – of statistical models like regression models. ESS can explain how well a model's predictions match actual data.

Why should I care about the error sum of squares (ESS)?

In marketing research, understanding error sum of squares calculations can provide insights into the accuracy of predictive models. An analysis of ESS can illustrate what’s behind the unexplained variability in a research model. That information can help marketing professionals refine strategies, make better predictions and optimize decision-making.