Abstract
The previous chapter dealt with tools for monitoring processes and detecting physical instabilities. The goal of using these is finding the source(s) of any process upsets and removing them, creating a process that is consistent/repeatable/ predictable in its pattern of variation. When that has been accomplished, it then makes sense to summarize that pattern of variation graphically and/or in terms of numerical summary measures. These descriptions of consistent process behavior can then become the bases for engineering and business decisions about if, when, and how the process should be used. This chapter discusses methods for characterizing the pattern of variation exhibited by a reasonably predictable system. Section 4.1 begins by presenting some more methods of statistical graphics (beyond those in Sect. 1.5) useful in process characterization. Then Sect. 4.2 discusses some “Process Capability Indices” and confidence intervals for them. Next, prediction and tolerance intervals for measurements generated by a stable process are presented in Sect. 4.3. Finally, Sect. 4.4 considers the problem of predicting the variation in output for a system simple enough to be described by an explicit equation, in terms of the variability of system inputs.
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Vardeman, S.B., Jobe, J.M. (2016). Process Characterization and Capability Analysis. In: Statistical Methods for Quality Assurance. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-79106-7_4
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