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The Shapiro-Wilk test compares a set of measures against the Normal distribution.
The test statistic is calculated as:
W = (SUM(aixi))2 / SUM(xi - x-bar)
where: ai is a constant based on the
If W is not small, then a Normal distribution may be concluded.
Shapiro-Wilk may be used before Parametric tests, to ensure the data being used has a Normal distribution.
Shapiro-Wilk is an improvement on the more general Kolmogorov-Smirnov curve-fitting algorithm.
Shapiro, S. S. and Wilk, M. B. (1965). "An analysis of variance test for normality (complete samples)", Biometrika, 52, 3 and 4, pages 591-611.
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