Quality Control Charts<sup>1</sup>

Shewhart, W A

doi:10.1002/j.1538-7305.1926.tb00125.x

Cited by 272 publications

(131 citation statements)

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“…Using values of 40 and 9 for σ 2 and τ 00 in the unconditional model in our simulation produced an intra-class correlation (ICC) of .19, which is consistent with the results of Hedges and Hedberg (2007). The covariance component τ 01 was set to 0 based on simulation evidence that this value typically has little impact on the number of clusters (Maas & Hox, 2004Zhang, 2005). The resulting pool of inputs in our exemplar study was specified as J = 10, 20, 30, 40, 50 (number of clusters), n j = 18, 29, 34, 44, 60, 68 (within-cluster sample sizes), and distribution = BVN ~ 09 40 0…”

Section:   supporting

confidence: 67%

“…Shewhart (1926) developed the control charts tool to identify when a process was producing a good or a defective product. Today, many control charts have developed to ensure quality through the control of certain characteristics of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang and Chen (2005) presented an extension of the exponentially weighted moving average (EWMA) technique to a DEWMA technique. These two DEWMA techniques are the same, and so are their conclusions.…”

mentioning

confidence: 99%

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Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. IntroductionComputer simulation studies represent an important tool for investigating statistical procedures difficult or impossible to study using mathematical theory or real data. Descriptors of these studies vary (e.g., statistical experiment, Monte Carlo simulation, computer experiment), but the examples of Hoaglin and Andrews (1975) and Hauck and Anderson (1984) are followed here with use of the term simulation studies. Extensive descriptions of simulation studies can be found in Lewis and Orav (1989) and Santner, Williams, and Notz (2003). In the behavioral sciences simulation studies have been used to study a wide array of statistical methods (e.g., Cribbie, Fiksenbaum, & Wilcox, 2012; Depaoli, 2012; Enders, Baraldi, & Cham, 2014; Hu & Bentler, 1999; Tomarken & Serlin, 1986). The general goal of these studies is to provide evidence of the behavior of statistical methods under a variety of data conditions that improves statistical practice and informs future statistical research. The goal here is to encourage methodological researchers to treat these studies as statistical sampling EXPERIMENTAL DESIGN AND DATA ANALYSIS IN SIMULATION 4 experiments subject to established principles of experimental design and data analysis.An underappreciated facet of simulation studies in statistics is their role in enhancing the reproducibility of scientific findings. The importance of reproducibility has gained momentum in numerous scientific arenas because of growing evidence that many findings cannot be replicated (Stodden, 2015). Concerns over reproducibility and the role of statistics were captured in Statistics and science: A report of the London workshop on the future of the statistical sciences (2014) which noted: "The reproducibility problem goes far beyond statistics, of course, because it involves the entire reward structure of the scientific enterprise. Nevertheless, statistics is a very important ingredient in both the problem and the remedy." (p. 27) Simulati...

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Section:   supporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

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Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

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“…A control chart can assist in deciding which type of variation is present. The control chart was initiated by Shewhart in the 1920s (Shewhart, 1926). A control chart consists of a central line, upper control limit, lower control limit and plotted data points (Figure 1).…”

Section: Variation and Control Chartsmentioning

confidence: 99%

Factors influencing control charts usage of operational measures

Öberg

Hammersberg

Fundin

2017

MBE

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download.

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“…The Shewhart (1926) methods have been used for c-chart and Poisson cumulative sum control charts, respectively. A Poisson cumulative sum accumulates the difference between an observed value and a reference value.…”

Section: Assessment Of Data Analysismentioning

confidence: 99%

Comparative analysis of Bayesian and maximum likelihood estimators in change point problems with Poisson process

Kitabo¹,

Kim

2015

Journal of the Korean Data and Information Science Society

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Nowadays the application of change point analysis has been indispensable in a wide range of areas such as quality control, finance, environmetrics, medicine, geographics, and engineering. Identification of times where process changes would help minimize the consequences that might happen afterwards. The main objective of this paper is to compare the change-point detection capabilities of Bayesian estimate and maximum likelihood estimate. We applied Bayesian and maximum likelihood techniques to formulate change points having a step change and multiple number of change points in a Poisson rate. After a signal from c-chart and Poisson cumulative sum control charts have been detected, Monte Carlo simulation has been applied to investigate the performance of Bayesian and maximum likelihood estimation. Change point detection capacities of Bayesian and maximum likelihood estimation techniques have been investigated through simulation. It has been found that the Bayesian estimates outperforms standard control charts well specially when there exists a small to medium size of step change. Moreover, it performs convincingly well in comparison with the maximum likelihood estimator and remains good choice specially in confidence interval statistical inference.

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Quality Control Charts¹

Cited by 272 publications

References 0 publications

Vol. 16, No. 2 (Full Issue)

Vol. 16, No. 2 (Full Issue)

Factors influencing control charts usage of operational measures

Comparative analysis of Bayesian and maximum likelihood estimators in change point problems with Poisson process

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Quality Control Charts1

Cited by 272 publications

References 0 publications

Vol. 16, No. 2 (Full Issue)

Vol. 16, No. 2 (Full Issue)

Factors influencing control charts usage of operational measures

Comparative analysis of Bayesian and maximum likelihood estimators in change point problems with Poisson process

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Quality Control Charts¹