Confidence-Reliability Calculations and Statistically Valid Sample Sizes

Overview:

The webinar 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 webinar. A final discussion is provided on how to introduce the methods into a company.

Why should you attend: 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 Covered in the Session:

• 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

Who Will Benefit:

• QA/QC Supervisor
• Process Engineer
• Manufacturing Engineer
• QC/QC Technician
• Manufacturing Technician
• R&D Engineer

Speaker Profile

John N. Zorich has spent almost 40 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 next 15 years were as a consultant in the areas of QA/QC and Statistics. These last few years were as a trainer and consultant in the area of Applied Statistics only. 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 applied statistics includes having given annual 3-day seminars for many years at Ohlone College (San Jose CA), and previously having given that same course for several years for Silicon Valley ASQ Biomedical. He's given numerous statistical seminars at ASQ meetings and conferences. And he creates and sells validated statistical software programs that have been purchased by more than 110 companies, world-wide.

Event link:

https://www.compliance4all.com/webinar/-502878LIVE?channel=eventsget_2020_SEO

Contact Info

Netzealous LLC, DBA -Compliance4all

Email: [email protected]

Phone: +1-800-447-9407

Website: https://www.compliance4all.com/

Others Details

Contact Info Netzealous LLC, DBA -Compliance4all Email: [email protected] Phone: +1-800-447-9407 Website: https://www.compliance4all.com/