Confidence / Reliability Calculations & Statistically Valid Sample Sizes

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All manufacturing and development companies perform testing and/or inspections that involve concluding whether or not a product or lot is acceptable vs. design or QC specifications. Such test/inspections may occur during design verification/validation or during incoming or final QC.

The most informative method for analyzing the data that results from such activities is the calculation of the product’s or lot’s “reliability” at a chosen “confidence” level (where “reliability” means “in-specification”). Such a method produces information that is more valuable than simply that the given product or lot “passed” (as is the case when “AQL Attribute Sampling Plans” are used) or a % in-specification statement without any corresponding confidence statement (as is the case with AQL Variables Sampling Plans and with Process Capability calculations).

The output of a “Confidence/Reliability” calculation is a definitive statement that the given product or lot has a specific % in-specification, which conclusion we can state with a specific level of confidence (e.g., 95% confidence of 99% reliability, or 90% confident of 93% reliability”).

Areas to be Covered:

The seminar begins with a discussion of relevant regulatory requirements, as motivation for calculating “confidence/reliability”. Then, some vocabulary and basic concepts are discussed.

Next, detailed descriptions are given for how to calculate confidence/reliability for data that is either pass/fail (i.e., “attribute” data), normally-distributed measurement data, non-normally distributed measurement data that can be transformed into normality, or non-normally distributed measurement data that cannot be transformed into normality. Spreadsheets are shown as examples of how to implement the methods described in the seminar. A final discussion is provided on how to introduce the methods into a company.

Who will Benefit:

Regulatory Requirements
Vocabulary and Concepts
Attribute Data
Normal Data
Normal Probability Plotting
Non-Normal Data that can be normalized
Reliability Plotting (for data that cannot be normalized)
Implementation Recommendations

John N. Zorich

John 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.

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  • Login Information with Password to join the session, 24 hours prior to the webinar
  • Presentation Handout in .pdf format
  • Presentation from the Speaker
  • Feedback form
  • Certificate of Attendance
  • Recording access Information with Password to view the webinar, will be sent 24 hours after the completion of the Live webinar.
  • Presentation Handout in .pdf format
  • Certificate of Attendance