Process Capability Analysis of Extremely Non-Normal Data
Overview:
Reliability Plotting 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. Why should you Attend:
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 best 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 % in-specification, 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. Areas Covered in the Session:
Definitions
How to create a reliability plot
How to use it to determine reliability
Example, using typical data
Exact vs. Interval plotting
Examples using data from: mixed distributions, highly replicated values, or censored studies
Comparison to use of K-tables, etc.
Who Will Benefit:
QA/QC Supervisor
Process Engineer
Manufacturing Engineer
QC/QC Technician
Manufacturing Technician
R&D Engineer
Speaker Profil:
John N. Zorich
has spent 35 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the last 15 years were as consultant in the areas of QA/QC and Statistics. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. His experience as an instructor in statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.
Contact Detail:
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