Confidence/Reliability & Tolerance Calculations for Extremely Non-Normal Data

Instructor: John N Zorich
Product ID: 705908
Training Level: Intermediate
  • 31
  • January 2019
    Thursday
  • 10:00 AM PST | 01:00 PM EST
    Duration: 90 Min
Standard methods of statistical analysis of process capabilities require normally distributed data or transformations to normality. When that is impossible, due to data having a bimodal or other distribution, the only practical alternative to calculating a high level of confidence/reliability or an acceptable Tolerance limit is to use Reliability Plotting.

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January 31, Thursday 10:00 AM PST | 01:00 PM EST | Duration: 90 Min

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Read Frequently Asked Questions

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 Webinar:

  • 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
Instructor Profile:
John N Zorich

John N Zorich
Statistical Consultant and Trainer, Statistical Consultant

John 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.

Topic Background:

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.

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