Process Capability Analysis Of Extremely Non-normal Data webinar at fremont 2017

OVERVIEW:

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.

Product Id : 501101
Instructor : John N. Zorich

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 Session:

• 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

IMPORTANT DATE:

Webinar Date: Tuesday, January 10, 2017 | 10:00 AM PST | 01:00 PM EST  

Duration: 90 Minutes

Others Details

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