Difference between revisions of "Durbin-Watson"
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*The Durbin-Watson test uses the following statistic: | *The Durbin-Watson test uses the following statistic: | ||
| − | <math>d=\frac{\sum_{i=2}^n (e_i- | + | <math>d=\frac{\sum_{i=2}^n (e_i-e_{i-1})^2)}{\sum_{i=1}^n (e_i)^2}</math>. |
Revision as of 05:27, 3 May 2017
DURBINWATSONTEST(DataRange,ConfidenceLevel,NewTableFlag))
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle DataRange} is the array of x and y values.
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle ConfidenceLevel} is the value of alpha from 0 to 1.
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle NewTableFlag} is either TRUE or FALSE. TRUE for getting results in a new cube. FALSE will display results in the same cube
Description
- 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.
Assumptions
1. The error terms are independent of each other.
- The Durbin-Watson test uses the following statistic:
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle d=\frac{\sum_{i=2}^n (e_i-e_{i-1})^2)}{\sum_{i=1}^n (e_i)^2}} .