We often sample from a process or population in order to make an inference about the process based on the sample results. Selecting appropriate sample sizes often vexes many practitioners.
This webinar discusses many issues present in any sample size determination. The webinar also discusses several common applications that require an appropriate sample size determination including estimation of product/process performance characteristics, hypothesis tests, acceptance sampling, Statistical Process Control charts, and reliability demonstration.
When selecting sample sizes, it is important to align the statistical properties of the estimate or test with practical considerations. More data is not always better. Numerous examples are provided to illustrate the key concepts and applications.
Why you should Attend:
The webinar will provide important considerations when selecting sample sizes for specific applications. The knowledge gained by attending the webinar will allow practitioners to consider the implications of sample size selection prior to conducting the study and ensure that the information obtained can be useful for decision making.
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.
Mr. Wachs is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.
Who Should Attend
- R&D Personnel
- Product Development Personnel
- Quality Personnel
- Lab Testing Personnel
- Operations/Production Managers
- Quality Assurance Managers, Engineers
- Process or Manufacturing Engineers or Managers
- Program or Product Managers
- Business Analysts
- Process Improvement Personnel