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Why Should You Attend:
This webinar discusses methods for estimating process capability for both normal and non-normal data. Pre-requisites for estimating process capability (e.g. establishing process stability) are discussed first. Distributions are briefly described and methods for estimating ppm levels are presented. The use and limitations of common process capability indices (e.g. Cpk and Ppk) are discussed. It is vital that appropriate methods are used for estimating capability when the data is not well described by a normal distribution. Failure to do so often results in overly optimistic process capability estimates. Methods for testing for normality are discussed. Both transformations and distribution fitting are presented as methods to assess capability for non-normal data. The webinar includes several examples to illustrate the methods.
Areas Covered in the Webinar:
Who Will Benefit:
The target audience includes personnel involved in product/process development and manufacturing
Physical CD-DVD of recorded session will be despatched after 72 hrs on completion of payment
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.