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A Study on Optimal Subgroup Size in Estimating Variance of Small Autocorrelated Samples  

Lee, Jong-Seon (Department of Statistics, Inha University)
Lee, Jae-June (Department of Statistics, Inha University)
Bae, Soon-Hee (Department of Statistics, Inha University)
Publication Information
Journal of Korean Society for Quality Management / v.35, no.2, 2007 , pp. 106-112 More about this Journal
Abstract
In statistical process control, it is assumed that the process data are independent. However, most of chemical processes such as semi-conduct processes do not satisfy the assumption because of presence of autocorrelation between process data. It causes abnormal out of control signal in the process control and misleading estimation in process capability. In this study, we adopted Shore's method to solve the problem and propose an optimal subgroup size to estimate the variance correctly for AR(1) processes. Especially, we focus on finding an actual subgroup size for small samples based on simulation study.
Keywords
Autocorrelation; Average Run Length; AR(1); Optimal Subgroup Size;
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