Module 18C

# Module 18C - IE 361 Module 18 Process Capability Analysis...

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IE 361 Module 18 Process Capability Analysis: Part 2 Reading: Section 5.3, Statistical Quality Assurance Methods for Engineers Prof. Steve Vardeman and Prof. Max Morris Iowa State University Vardeman and Morris (Iowa State University) IE 361 Module 18 1 / 12

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"Capability" and Future Values The measures 6 σ , C p , and C pk considered in Module 17 attempt the summarize process "capability" in terms of a function of process parameters (and where appropriate, speci°cations for individual outcomes). In this module we consider methods of characterizing process output that focus what can be said about the values of individual future process outcomes based on data in hand (rather than about process summary measures). Vardeman and Morris (Iowa State University) IE 361 Module 18 2 / 12
"One More Value" or "Most of the Process Distribution" If I KNOW process parameters, making statements about future individual values generated by the process is a matter of simple probability calculation. Suppose, for example, that I model individual values as normal with μ = 7 and σ = 1. there±s a "90% chance" the next x is between 5 . 355 and 8 . 645 90% of the process distribution is between 5 . 355 and 8 . 645 But what if I only have a sample, and not the process parameters? What then can I say? When one has to use a sample to get an approximate picture of a process, it is important to hedge statements in light of sample variability/uncertainty . . . this can be done for normal processes using x and s in general, using the sample minimum and/or maximum values Vardeman and Morris (Iowa State University) IE 361 Module 18 3 / 12

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Methods for Normal Processes Prediction Limits We consider °rst methods for normal processes. (Just as we cautioned in Module 17 that the methods for estimating capabilities are completely unreliable unless the data-generating process is adequately described by a normal model, so too does the e/ectiveness of the next 2 formulas depend critically on the normal assumption being appropriate.)
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