The Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation (a relationship between values separated from each other by a given time lag) in the residuals from a regression analysis. It’s named after statisticians James Durbin and Geoffrey Watson.
The value of this statistic ranges from 0 to 4, where a value around 2 suggests no autocorrelation. If the statistic deviates significantly from 2, it signals positive or negative autocorrelation. A value below 2 indicates positive autocorrelation, while a value above 2 shows negative autocorrelation.
In essence, the Durbin-Watson statistic helps us understand if there are any patterns in the residuals that we should be aware of. This is crucial because one of the assumptions of linear regression is that the residuals are not autocorrelated. Violation of this assumption can lead to inefficient parameter estimates.
