Which measure accounts for the variability of residuals in forecasting?

The measure that accounts for the variability of residuals in forecasting is the residual sum of squares. This metric captures the total quantity of variability in the data points that is not explained by the model. Specifically, it measures the sum of the squares of the differences between the observed values and the values predicted by the model, known as the residuals.

By focusing on the squared differences, it penalizes larger discrepancies more than smaller ones, thereby giving a comprehensive view of how tightly the data points cluster around the predicted values. A lower residual sum of squares indicates a better fit of the model to the data, whereas a higher value suggests that the model's predictions diverge significantly from the actual observations.

This concept is fundamental in regression analysis and plays a crucial role in various statistical techniques for assessing how well a model fits the data, guiding improvements in forecasting accuracy.