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Home » Podcast Episodes » Accendo Reliability Webinar Series » SPC Sample Size

by Steven Wachs Leave a Comment

SPC Sample Size

SPC Sample Size Impact on SPC

Presented by Steven Wachs

The purpose of control charting is to detect significant process changes when they occur.

One important consideration in the selection and design of control charts (that is often overlooked) is the determination of an appropriate sample size.

A sample size should be selected that will align the statistical properties of the control chart with what are practically significant changes.

That is, effective control charts will detect process changes that have a practical significance while minimizing signals resulting from smaller changes of minimal concern.

In this webinar, we explore the impact that chart selection and sample size have on chart sensitivity (the ability to detect changes).

The factors to be considered when determining the sample size and examples of an appropriate sample size for a typical application are presented.

The relationship between sample size and sampling frequency is also discussed.

Finally, the use of OC curves to assess the risks of undetected process changes for a given sample size is presented.

Webinar Topics:

  • Purpose of Control Charts
  • Variation: Individuals vs. Averages
  • Detecting Process Shifts
  • Type II Errors
  • Factors affecting sample size
  • Calculating sample size for traditional (X-bar) charts
  • Sample size and sampling frequency tradeoffs
  • OC Curves for assessing risk

This Accendo Reliability webinar was originally broadcast live on 21 May 2014.

Accendo Reliability Webinar Series
Accendo Reliability Webinar Series
SPC Sample Size
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To view the recorded video and slides, visit the webinar page.

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Filed Under: Accendo Reliability Webinar Series, The Reliability FM network

About Steven Wachs

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve 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.

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