Shapiro-Wilk Statistic
If the sample size is less than or equal to 2000 and you specify the NORMAL option, PROC UNIVARIATE computes the Shapiro-Wilk statistic, W (also denoted as Wn to emphasize its dependence on the sample size n). The W statistic is the ratio of the best estimator of the variance (based on the square of a linear combination of the order statistics) to the usual corrected sum of squares estimator of the variance (Shapiro and Wilk 1965). When n is greater than three, the coefficients to compute the linear combination of the order statistics are approximated by the method of Royston (1992). The statistic W is always greater than zero and less than or equal to one .

Small values of W lead to the rejection of the null hypothesis of normality. The distribution of W is highly skewed. Seemingly large values of W (such as 0.90) may be considered small and lead you to reject the null hypothesis. The method for computing the p-value (the probability of obtaining a W statistic less than or equal to the observed value) depends on n. For n=3, the probability distribution of W is known and is used to determine the p-value. For n>4, a normalizing transformation is computed:
The values of , , and  are functions of n obtained from simulation results. Large values of Zn indicate departure from normality, and since the statistic Zn has an approximately standard normal distribution, this distribution is used to determine the p-values for n>4.
有一个公式是图片格式
自己去HELP里面搜吧
很清楚的解释

我找了下，好像没有，我现在等着急用，不知道版主所说的是在哪？

W=\frac{\sum_{i=1}^n a_iX_{(i)}^2}{\sum_{i=1}^n (X_i-\overline{X})^2},\ \sum_{i=1}^na_i^2=1.
放到latex软件中就可知道