A tool designed to compute the spread of error terms in a regression model. It quantifies the typical distance that observed data points fall from the regression line or surface. For instance, if a model predicts housing prices based on square footage, this calculation would reveal how much, on average, the actual sale prices deviate from the prices predicted by the model.
The magnitude of this value provides insight into the overall fit of a statistical model. A smaller value suggests that the model’s predictions are generally close to the observed data, indicating a better fit and higher predictive accuracy. Conversely, a larger value signals greater variability and potentially a less reliable model. Historically, calculating this metric was a tedious manual process, but advancements in computing have led to readily available, efficient solutions that simplify the assessment of model quality.
