Difference between revisions of "Durbin-Watson"

Revision as of 05:23, 3 May 2017

DURBINWATSONTEST(DataRange,ConfidenceLevel,NewTableFlag))

$DataRange$ is the array of x and y values.
$ConfidenceLevel$ is the value of alpha from 0 to 1.
$NewTableFlag$ is either TRUE or FALSE. TRUE for getting results in a new cube. FALSE will display results in the same cube

This function gives the test statistic of the Durbin-Watson test.
The test is used to detect the presence of autocorrelation in the residuals.
Autocorrelation means that adjacent observations are correlated.
If they are correlated, then least-squares regression underestimates the standard error of the coefficients.

1. The error terms are independent of each other.

Failed to parse (syntax error): {\displaystyle d=\frac{sum_{i=2}^n (e_i-e_i-1)^2)}\sum_{i=1}^n (e_i)^2}} .

Revision as of 05:22, 3 May 2017 (view source) Sahiti (talk \| contribs) (Created page with "<div style="font-size:25px">'''DURBINWATSONTEST(DataRange,ConfidenceLevel,NewTableFlag))'''</div><br/> <math>DataRange</math> is the array of x and y values. <math>Confidenc...")		Revision as of 05:23, 3 May 2017 (view source) Sahiti (talk \| contribs) Newer edit →
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	*The Durbin-Watson test uses the following statistic:		*The Durbin-Watson test uses the following statistic:
−	<math>W=\frac{sum_{i=2}^n (e_i-e_i-1)^2)\sum_{i=1}^n (e_i)^2}</math>.	+	<math>d=\frac{sum_{i=2}^n (e_i-e_i-1)^2)}\sum_{i=1}^n (e_i)^2}</math>.