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Sample Size Determination for Design Validation Activities

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Website | Edit Freely

Category Production Supervisors, Supplier Quality Personnel, Quality Engineering

Deadline: December 10, 2018 | Date: December 12, 2018

Venue/Country: Online, U.S.A

Updated: 2018-11-13 17:57:50 (GMT+9)

Call For Papers - CFP


Statistical Methods are typically used to ensure that product performance, quality, and reliability requirements are met during the Design Validation phase of product development. This webinar discusses common elements of sample size determination and several specific sample size applications for various design validation activities including Reliability Demonstration/Estimation, Acceptance Sampling, and Hypothesis Testing.

Why should you Attend

Sample sizes have a significant impact on the uncertainty in estimates of key process performance characteristics. To have high confidence in results,sufficient sample sizes must be used. Potential problems should be uncovered during Design Validation, prior to launching a product. Failure to do so may result in customer dissatisfaction, excessive warranty, costly recalls,or litigation.

Participants in the webinar will be able to understand the impact of sample sizes on the results from various statistical analysis methods commonly used during Design Validation.

Areas Covered in the Session

Populations, Samples, Data Types,and Basic Statistics

Common Elements of Sample Size Determination

Design Validation Applications

Sample Sizes for Reliability Demonstration (Pass/Fail Outcomes)

Sample Sizes for Reliability Estimation

Sample Sizes for Estimating Proportion Failing (Pass/Fail Test Outcomes)

Sample Sizes for Acceptance Sampling / Lot Disposition

Other Common Sample Size Applications (Hypothesis Testing, Equivalence Testing)

Who Will Benefit

The Target Audience includes Personnel involved in Product/Process Development and Manufacturing

Quality Personnel

Product Design/Development Personnel

Manufacturing Personnel

Operations / Production Managers

Production Supervisors

Supplier Quality Personnel

Quality Engineering

Quality Assurance Managers, Engineers

Process or Manufacturing Engineers or Managers

Speaker Profile

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.