Overview
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 should you 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.
Areas Covered in the Session
- Population and Samples
- Basic Statistics
- Common Applications requiring sample size determination
- Hypothesis Testing
- Estimation of parameters
- Acceptance Sampling Plans
- Statistical Process Control Charts
- Reliability Demonstration Testing
Who Will Benefit
- 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
- Management
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.
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.
He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.