COMPARISON METHOD PROCESS CAPABILITY INDICES FOR NORMAL AND NON-NORMAL DATA BY USE SIMULATING.
Wakkaa Ali Hadba
Abstract
Wakkaa Ali Hadba
Abstract
In quality control assume that the distribution of their process output is normal. The definition and estimation of (pci) indices are usually based on the assumption that the production process under normal distribution. But, in most practical cases this assumption is not valid and the distribution of the quality characteristics may follow non-normal distributions such as Weibull ,Lognormal , and Exponential distribution). One can see the difference between the non-normal process capability indices and the normal process capability indices by overlooking its theoretical distributions .This paper provides a decision on how to calculate the process capability indices for normal and find process capability indices another step transformation data by (Box – Cox( and Johnson transformation and comparison between two methods.
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In quality control assume that the distribution of their process output is normal. The definition and estimation of (pci) indices are usually based on the assumption that the production process under normal distribution. But, in most practical cases this assumption is not valid and the distribution of the quality characteristics may follow non-normal distributions such as Weibull ,Lognormal , and Exponential distribution). One can see the difference between the non-normal process capability indices and the normal process capability indices by overlooking its theoretical distributions .This paper provides a decision on how to calculate the process capability indices for normal and find process capability indices another step transformation data by (Box – Cox( and Johnson transformation and comparison between two methods.
Key concepts: Log-normal distribution, Normal distribution, Process capability, Weibull distribution, Process capability index, Transformation (genetics), Generalized normal distribution, Truncated normal distribution