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I would like to ask anyone who has an idea on Cpk computation methods that are robust and can work better on SKEWED distribution.
Classical method which is min (Cpu, Cpl) tends to be very sensitive to outliers especially if the parameters that we're assessing is one-sided (the higher reading the better) - that is why, even though the minimum of the data is way above the lower spec limit but because of high readings that tend to make the distribution skewed to the right, Cpk which is equal to Cpl (in this case of one-sided LSL) is still below 1 which is clearly misleading.
Currently i'm looking at the pearson curves method which utilizes percentiles - but i am not so sure of the appropriate percentile value for sample size of 30.
Can anyone advice for a much better alternative Cpk computation method for skewed data?
Classical method which is min (Cpu, Cpl) tends to be very sensitive to outliers especially if the parameters that we're assessing is one-sided (the higher reading the better) - that is why, even though the minimum of the data is way above the lower spec limit but because of high readings that tend to make the distribution skewed to the right, Cpk which is equal to Cpl (in this case of one-sided LSL) is still below 1 which is clearly misleading.
Currently i'm looking at the pearson curves method which utilizes percentiles - but i am not so sure of the appropriate percentile value for sample size of 30.
Can anyone advice for a much better alternative Cpk computation method for skewed data?
