DurbinWatsonteszt

Durbin-Watson test is a statistical test used to detect the presence of autocorrelation in the residuals from a regression analysis. Autocorrelation occurs when the residuals are not independent, which can violate the assumptions of ordinary least squares (OLS) regression and lead to inefficient or biased estimates. The Durbin-Watson test is particularly useful in time series data or data where the order of observations matters.

The test statistic, d, ranges from 0 to 4. A value of 2 indicates no autocorrelation. Values significantly less than 2 suggest positive autocorrelation, while values significantly greater than 2 suggest negative autocorrelation. The critical values for the test depend on the number of observations and the number of regressors in the model.

The Durbin-Watson test is simple to compute and interpret, making it a popular choice for practitioners. However, it has limitations. It is most effective for detecting first-order autocorrelation and may not be reliable for higher-order autocorrelation. Additionally, the test assumes that the errors are normally distributed, which may not always be the case.

In practice, the Durbin-Watson test is often used in conjunction with other diagnostic tests to assess the validity of regression assumptions. If autocorrelation is detected, alternative methods such as generalized least squares (GLS) or time series models may be used to correct for it.