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 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.
Contact the event managers listed below for more information about how you can participate at the Confidence-Reliability Calculations and Statistically Valid Sample Sizes - webinar by Compliance4all.
|Conference/Event Dates:||03/03/2015 - 03/03/2015|
|Primary Industry:||Manufacturing - General|
|Other Industries:||Manufacturing, Manufacturing - General|
|Audience:||Who Will Benefit:
|Booth Size||Booth Cost||Available Amenities|
|No exhibiting at this event.||Electricity:||n/a|
|Marketing Vehicles Allowed:||n/a|
|Other Booth Sizes Available: n/a|
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