![]() ![]() You can do the same for an exponential distribution and see that, while the departure from normality is more pronounced, it isn't terrible. You should see that the distribution of the averages strongly resembles a normal distribution. construct a normal probability plot with the averages draw 1000 samples of size 10 from a uniform distribution To see this in action, using your statistical software, conduct this experiment: On the other hand, one should not rule out X-bar (averages) charts for non-normal data, as they can be relatively robust against some forms of non-normality, especially skewness, kurtosis, or censoring. One could substitute 99% control limits calculated using Chebyshev's inequality, but this would be relatively insensitive to shifts in mean. This is because the three sigma control limits would not be appropriate for a variable with a significantly non-normal distribution. If I am in error, someone please correct me.įirst, I believe the Individual and Moving Range approach would not be effective. ![]()
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