Statistics Definitions > Durbin Watson Test & Coefficient
What is The Durbin Watson Test?
The Durbin Watson Test is a measure of autocorrelation (also called serial correlation) in residuals from regression analysis. Autocorrelation is the similarity of a time series over successive time intervals. It can lead to underestimates of the standard error and can cause you to think predictors are significant when they are not. The Durbin Watson test looks for a specific type of serial correlation, the AR(1) process.
The Hypotheses for the Durbin Watson test are:
H0 = no first order autocorrelation.
H1 = first order correlation exists.
(For a first order correlation, the lag is one time unit).
The Durbin Watson test reports a test statistic, with a value from 0 to 4, where:
- 2 is no autocorrelation.
- 0 to <2 is positive autocorrelation (common in time series data).
- >2 to 4 is negative autocorrelation (less common in time series data).
A rule of thumb is that test statistic values in the range of 1.5 to 2.5 are relatively normal. Values outside of this range could be cause for concern. Field(2009) suggests that values under 1 or more than 3 are a definite cause for concern.
The Durbin Watson test is rarely used and is considered by some to be archaic. It requires the use of tables, which you can find here. Here’s an except of the table at the 5% alpha level (the most common significance level used):
As the tables are so cumbersome, you’ll want to use technology to find the DW statistic.
- In Minitab:
Click Stat > Regression > Regression > Fit Regression Model. Click “Results,” and check the Durbin-Watson statistic.
- SAS: Find directions here on the UCLA website.
- MATLAB: The procedure can be found here on the Mathworks site.
- SPSS: From the main regression dialog box, click Statistics. Check the box for Durbin-Watson (in the Residuals section of Linear Regression Statistics).
Field, A.P. (2009). Discovering statistics using SPSS: and sex and drugs and rock ‘n’ roll (3rd edition). London:Sage.
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