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One method of detecting autocorrelation is to examine a plot of the residuals against time. The previous question provides benchmark images to help you assess residual plots for first-order autocorrelation, but a residual plot is not always easy to read. Another method to detect the presence of first-order autocorrelation is to conduct a hypothesis test using the Durbin-Watson test statistic d. Consider this hard-to-interpret residual plot from a multiple regression with four independent variables that you estimate using 60 time-series observations. You can't tell if the residuals exhibit negative first-order autocorrelation. The Durbin-Watson test statistic reported in your regression output is d = 2.070. Residual 3 2 1 0 -1 -2 -3 -4 Time A portion of the table of critical values for a Durbin-Watson test from your text is reproduced as follows. Conduct a one-sided test for negative autocorrelation at the .05 level of significance. Significance Points of dL and dU: ? = 0.05 p = 1 p = 2 p = 3 p = 4 p = 5 n dL dU dL dU dL dU dL dU dL dU 50 1.50 1.59 1.46 1.63 1.42 1.67 1.38 1.72 1.34 1.77 70 1.58 1.64 1.55 1.67 1.52 1.70 1.49 1.74 1.46 1.77 What do you conclude from the Durbin-Watson test? Positive autocorrelation is present. There is no evidence of negative autocorrelation. Negative autocorrelation is present. The test is inconclusive. If you identify autocorrelation, which of the following methods are appropriate for correcting or reducing the autocorrelation? Check all that apply. Respecify the model to include an interaction term. Transform one or more of the independent variables.

          One method of detecting autocorrelation is to examine a plot of the residuals against time.

The previous question provides benchmark images to help you assess residual plots for first-order autocorrelation, but a residual plot is not always easy to read. Another method to detect the presence of first-order autocorrelation is to conduct a hypothesis test using the Durbin-Watson test statistic d.

Consider this hard-to-interpret residual plot from a multiple regression with four independent variables that you estimate using 60 time-series observations. You can't tell if the residuals exhibit negative first-order autocorrelation. The Durbin-Watson test statistic reported in your regression output is d = 2.070.

Residual
3
2
1
0
-1
-2
-3
-4
Time

A portion of the table of critical values for a Durbin-Watson test from your text is reproduced as follows. Conduct a one-sided test for negative autocorrelation at the .05 level of significance.

Significance Points of dL and dU: ? = 0.05
p = 1 p = 2 p = 3 p = 4 p = 5
n dL dU dL dU dL dU dL dU dL dU
50 1.50 1.59 1.46 1.63 1.42 1.67 1.38 1.72 1.34 1.77
70 1.58 1.64 1.55 1.67 1.52 1.70 1.49 1.74 1.46 1.77

What do you conclude from the Durbin-Watson test?
Positive autocorrelation is present.
There is no evidence of negative autocorrelation.
Negative autocorrelation is present.
The test is inconclusive.

If you identify autocorrelation, which of the following methods are appropriate for correcting or reducing the autocorrelation? Check all that apply.
Respecify the model to include an interaction term.
Transform one or more of the independent variables.

One method of detecting autocorrelation is to examine a plot of the residuals against time.

The previous question provides benchmark images to help you assess residual plots for first-order autocorrelation, but a residual plot is not always easy to read. Another method to detect the presence of first-order autocorrelation is to conduct a hypothesis test using the Durbin-Watson test statistic d.

Consider this hard-to-interpret residual plot from a multiple regression with four independent variables that you estimate using 60 time-series observations. You can't tell if the residuals exhibit negative first-order autocorrelation. The Durbin-Watson test statistic reported in your regression output is d = 2.070.

Residual
3
2
1
0
-1
-2
-3
-4
Time

A portion of the table of critical values for a Durbin-Watson test from your text is reproduced as follows. Conduct a one-sided test for negative autocorrelation at the .05 level of significance.

Significance Points of dL and dU: ? = 0.05
p = 1 p = 2 p = 3 p = 4 p = 5
n dL dU dL dU dL dU dL dU dL dU
50 1.50 1.59 1.46 1.63 1.42 1.67 1.38 1.72 1.34 1.77
70 1.58 1.64 1.55 1.67 1.52 1.70 1.49 1.74 1.46 1.77

What do you conclude from the Durbin-Watson test?
Positive autocorrelation is present.
There is no evidence of negative autocorrelation.
Negative autocorrelation is present.
The test is inconclusive.

If you identify autocorrelation, which of the following methods are appropriate for correcting or reducing the autocorrelation? Check all that apply.
Respecify the model to include an interaction term.
Transform one or more of the independent variables.

Added by Bruce F.

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