Check for outliers, equal variance, normality, and model validity

C. Check normality.

 Page up in Output Window until you see this:

Shapiro-Wilk W value should be above 0.90 for normality. In the above example, the 0.965 value shows that the data are very normal.

     If the W is between 0.8 and 0.9, then check the Pr < W. If it is
      > 0.05, then you would accept the null hypothesis that the data
      are normal. If it is < 0.05, then you should examine the
      normality plots.

     If the W is below 0.8, then a transformation is probably required.

       Normality plots show visual representations of the normal
       distribution. The easiest plots are versions of histograms that will
       show the symmetric bell-shaped normal curve. For example, check
       the Stem-Leaf plot (at right, notice bell-shaped red curve) or, in the        Graphics Window, the more traditional vertical representation        (below).

     If the plots are symmetric, bell-shaped, with a single peak, then
       normality can be accepted despite having a low W value above.

next >> ( continuing with Step C: Check for Normality )