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- W2294992015 abstract "ABSTRACTControl charts are effective tools for signal detection in both manufacturing processes and service processes. Much service data come from a process with variables having non-normal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, should not be properly used in such circumstances. In this paper, we propose a new variance chart based on a simple statistic to monitor process variance shifts. We explore the sampling properties of the new monitoring statistic and calculate the average run lengths (ARLs) of the proposed variance chart. Furthermore, an arcsine transformed exponentially weighted moving average (EWMA) chart is proposed because the ARLs of this modified chart are more intuitive and reasonable than those of the variance chart. We compare the out-of-control variance detection performance of the proposed variance chart with that of the non-parametric Mood variance (NP-M) chart with runs rules, developed by Zombade ..." @default.
- W2294992015 created "2016-06-24" @default.
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- W2294992015 date "2015-12-28" @default.
- W2294992015 modified "2023-09-25" @default.
- W2294992015 title "A new approach for monitoring process variance" @default.
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- W2294992015 doi "https://doi.org/10.1080/00949655.2015.1125901" @default.
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