Abstract
ABSTRACT In this article, we compare the statistical properties of , , and RWAV methods for estimation of variance of a process for quality control purposes. We investigate effects of non-normality for different estimators. Our results indicate that RWAV gives the best estimates of the standard deviations for both normal and non-normal processes. We recommend the construction of control charts with RWAV.
Highlights
Statistical process control (SPC) is useful for quality assurance in production and other operations
Estimation of the process standard deviation is important in SPC and is fundamental to the construction of control charts
Based on the evidence presented, we recommend the use of RWAV for both equal and unequal sample sizes
Summary
Statistical process control (SPC) is useful for quality assurance in production and other operations. The Shewhart control charts, i.e., X charts, R charts, and. Phase I is the chart construction phase, and Phase II is the monitoring phase. In Phase I, one needs to make sure that the underlying process is in control before he formally estimates the central line and control limits. We imply that one assumes (and verifies) that the underlying process is normal because the sample size employed in Shewhart control chart methods is usually small (less than 30 in most situations). In Phase II, the quality analyst monitors the process for assignable causes of variation associated with unexpected changes in the process parameters; i.e., the process mean and process variability (Shewhart, 1931). Any advantage an estimator may have relates to the accuracy of parameter estimates (Del Castillo, 1996)
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