To get a conclusion from the test, you can compare the displayed value for the Durbin-Watson statistic with the correct lower and upper bounds in the following table from Savin and White 1. The Durbin-Watson statistic determines whether or not the correlation between adjacent error terms is zero. Minitab assumes that the observations are in a meaningful order, such as time order. The Durbin-Watson statistic (D) is conditioned on the order of the observations (rows). Underestimated standard errors can make your predictors seem to be significant when they are not.įor example, the errors from a regression model on daily stock price data might depend on the preceding observation because one day's stock price affects the next day's price. If the errors are correlated, then least-squares regression can underestimate the standard error of the coefficients. Autocorrelation means that the errors of adjacent observations are correlated. Use the Durbin-Watson statistic to test for the presence of autocorrelation in the errors of a regression model.
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