What is acceptable Durbin-Watson?
An acceptable range is 1.50 – 2.50. Where successive error differences are small, Durbin-Watson is low (less than 1.50); this indicates the presence of positive autocorrelation.
What is the use of Durbin-Watson statistic?
The Durbin Watson statistic is a test statistic used in statistics to detect autocorrelation in the residuals from a regression analysis. The Durbin Watson statistic will always assume a value between 0 and 4. A value of DW = 2 indicates that there is no autocorrelation.
What is the significance of the Durbin Watson test?
The Durbin- Watson test statistic value is 0.24878. We want to test the null hypothesis of zero autocorrelation in the residuals against the alternative that the residuals are positively autocorrelated at the 1% level of significance.
When to use the Durbin Watson autocorrelation test?
The Durbin Watson statistic will always assume a value between 0 and 4. A value of DW = 2 indicates that there is no autocorrelation. One important way of using the test is to predict the price movement of a particular stock based on historical data. What is Autocorrelation?
What is the value of the Durban Watson statistic?
Interpreting the Durban Watson Statistic The Durban Watson statistic will always assume a value between 0 and 4. A value of DW = 2 indicates that there is no autocorrelation. When the value is below 2, it indicates a positive autocorrelation, and a value higher than 2 indicates a negative serial correlation.
How to calculate the Durbin Watson line of best fit?
Using the methods of a least squares regression to find the “line of best fit,” the equation for the best fit line of this data is: Y=−2.6268x+1,129.2Y={-2.6268}x+{1,129.2}Y=−2.6268x+1,129.2. This first step in calculating the Durbin Watson statistic is to calculate the expected “y” values using the line of best fit equation.