Residual

Definition

Residual is the difference between the observed value and the predicted value in a regression analysis. It measures the error or deviation of the actual data point from the model's estimate.

5 Must Know Facts For Your Next Test

1. Residuals are used to assess the goodness of fit in regression models.
2. A smaller residual indicates a more accurate prediction by the regression model.
3. The sum of residuals in a least squares regression line is always zero.
4. Residual plots help identify patterns that suggest departures from linearity.
5. Homoscedasticity assumes that residuals have constant variance across all levels of an independent variable.

Review Questions

• What does a residual represent in a regression analysis?
• Why is it important for residuals to be randomly distributed around zero?
• How can residual plots be used to assess the validity of a regression model?

Related terms

Homoscedasticity: A condition where residuals have constant variance across all levels of an independent variable.

Goodness of Fit: A measure that describes how well a statistical model fits a set of observations.

Least Squares Method: A standard approach in regression analysis to minimize the sum of squared residuals.