Durbin-Watson statistic

The Durbin-Watson statistic is a number that helps identify if there’s a pattern in the errors of a regression model over time or order. Specifically, it detects autocorrelation—when the errors (differences between observed and predicted values) tend to be similar to neighboring errors. A value close to 2 suggests no autocorrelation, while values toward 0 or 4 indicate positive or negative autocorrelation, respectively. Detecting autocorrelation is important because it can affect the accuracy of the model’s predictions and the validity of our statistical inferences.