How is Process Capability Analysis of Extremely Non-Normal Data Done?

The most informative method for analyzing the data that results from QC, Validation, or Engineering activities is the calculation of the product’s or lot’s “reliability” at a chosen “confidence” level (where “reliability” means “in-specification”). Such calculations are relatively simple when data is “normally distributed”; but if the data is non-normal and cannot be transformed to normality, then there is typically no simple way to calculate a reasonably accurate level of reliability.

In such a situation, the ideal method for determining reliability is called “Reliability Plotting”. The output of reliability plotting is a definitive statement that the given product or lot has a specific percentage in-specification and which conclusion can be stated with a specific level of confidence (e.g., 95% confidence of 99% reliability, or 90% confident of 93% reliability). Reliability plotting can be performed using an Excel spreadsheet and formulas found in almost any introductory statistics textbook.

A learning session Process Capability Analysis

A webinar that familiarizes participants with the concept of Reliability Plotting is being organized by Compliance4All, a very well-known provider of professional trainings for all the areas of regulatory compliance. John Zorich, Statistical Consultant & Trainer at Ohlone College & SV Polytechnic, will be the Director at this seminar. You can enroll for this webinar by logging on to http://www.compliance4all.com/control/w_product/~product_id=501027LIVE/~sel=LIVE/~John_N.%20Zorich/~Process_Capability_Analysis_of_Extremely_Non-Normal_Data

Reliability plotting in detail

John will familiarize participants with Reliability Plotting, which is a graphical technique that is a standard method described in some reliability textbooks. The method is used primarily for data that is problematic in one or more of the following ways: non-normal (e.g., a Fatigue-Life distribution), a mixture of distributions (e.g., the distribution looks bi-modal when arranged into a histogram), low precision (e.g., a large number of identical readings in a small sample size), and/or incomplete (e.g., when a study is terminated before all on-test devices can be measured, due either to measurement equipment limitations or due to time limitations). Reliability plotting can easily handle all such situations.

This method involves first creating a probability plot (Y = %cumulative vs. X = raw data). That step and all subsequent ones can easily and automatically be performed using an Excel spreadsheet.

At this webinar, John will cover the following areas:

o  Definitions

o  How to create a reliability plot

o  How to use it to determine reliability

o  Example, using typical data

o  Exact vs. Interval plotting

o  Examples using data from: mixed distributions, highly replicated values, or censored studies

o  Comparison to use of K-tables, etc.

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