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Capability analysis for non-normal data

Since a capability study makes the assumption that the data being analyzed is normally distributed , what can be done if the data is not normally distributed?

Usually if the data is not normally distributed, the process is not in control and a capability study is premature. However, in some cases the non-normal process is due to a measure that legitimately has only a single-sided specification . For example, if you are measuring flatness, the measurements can never be smaller than 0. In these cases, you will need to use Pearson curve fitting. Pearson curve fitting is a technique in which the distribution is compared to one of many theoretical distributions . If the data matches closely enough, it will pass a chi-square test and the capability indices will be useful. As with normally distributed data, if the data does not match one of the theoretical distributions, then the capability indices may be misleading and should not be used.

See also:
>> Can a process produce output within specifications?
>> Capability vs control
>> Normal data capability analysis
>> What is capability analysis and when is it used?
>> What are the capability indices?
>> Learning more about capability

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