Statistical Tolerance Intervals & Limits: What, Why, How, & Alternatives

Overview:
A Statistical Tolerance Interval (TI) is a range of values on either side of a sample average that includes a specified proportion of the population from which the sample was drawn; the likelihood that that range includes at least the specified proportion of the population is given by a specified % confidence. This webinar explains..

practical uses for TI's
theoretical foundation for TI's
how TI Limits are calculated
how to decide whether or not the raw sample data needs to be "transformed"
how to decide on which "transformation" to use
how to calculate TI Limits based upon "transformed" data
how to reverse-transform TI Limits back into units of the raw sample data
an alternative to TI's

Why you should Attend: An attendee will learn
what is a Statistical Tolerance Interval and how it differs from a Confidence Interval, a Prediction Interval, or a non-statistical Tolerance Interval
how to identify a distribution (e.g., Normality)
how to transform to Normality or other distribution
how to calculated Tolerance Interval Limits
when software programs are needed for calculations
what alternative to Statistical Tolerance Intervals is available

In the speaker's experience, most medical device and pharmaceutical companies use Statistical Tolerance Limits as part of their risk-management program. Typically, such companies do not understand the principles involved, and therefore tend to make judgement errors, especially when non-normal data is being analyzed; the purpose of this webinar is to prevent such mistakes.

Attendees will have access to speaker's website, for download of articles, example-SOP's, and demo-software on a variety of statistical topics.

Areas Covered in the Session:
Statistical Tolerance Limits
Non-statistical Tolerance Limits
Identification of Distributions
Data Transformations
Theoretical Basis of Statistical Tolerance Limits
Calculation of Statistical Tolerance Limits for raw data
Calculation of Statistical Tolerance Limits transformed non-normal data
When it is best to use Software Programs
Recommended Alternative to Tolerance Limits

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/-502902LIVE?channel=theconferencealerts_2020_SEO
Contact Info
Netzealous LLC, DBA -Compliance4all
Email: [email protected]
Phone: +1-800-447-9407
Website: https://www.compliance4all.com/