General control charts for attributes

Shore, Haim

doi:10.1080/07408170008967469

Cited by 18 publications

(25 citation statements)

References 15 publications

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“…By adding a simple constant term to both control limits, these limits now reflect the actual skewness of the monitoring statistic (Y ), with much improved average run length properties, as previously demonstrated 10 . Furthermore, certain attributes distributions, with skewness that does not allow application of the traditional Shewhart chart, may now be also included in this modified Shewhart chart (refer to Shore 10 for details).…”

Section: Non-normality In Attributes Datamentioning

confidence: 72%

“…Various solutions attempted to remedy this shortcoming of the Shewhart chart by suggesting alternative charts with non-symmetrical control limits. A review of some of these solutions is given in Woodall 9 and in Shore 10 . However, as indicated in the latter reference, with these solutions the actual skewness of the monitoring statistic (as measured by the third standardized moment) rarely enter in the calculation of the control limits, neither is it explicitly preserved in the approximating distribution used to determine the control limits.…”

Section: Non-normality In Quality and Reliability Engineering-a Discumentioning

confidence: 98%

“…In this section, we introduce the essentials of this approach. The introduction herewith pursues Shore 10 , and the reader is encouraged to refer to this source for further details and for numerical comparisons in the context of statistical process control (SPC).…”

Section: Non-normality In Attributes Datamentioning

confidence: 99%

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Non‐normal Populations in Quality Applications: a Revisited Perspective

Shore¹

2004

Quality & Reliability Eng

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Much research effort has recently been focused on methods to deal with nonnormal populations. While for weak non-normality the normal approximation is a useful choice (as in Shewhart control charts), moderate to strong skewness requires alternative approaches. In this short communication, we discuss the properties required from such approaches, and revisit two new ones. The first approach, for attributes data, assumes that the mean, the variance and the skewness measure can be calculated. These are then incorporated in a modified normal approximation, which preserves these moments. Extension of the Shewhart chart to skewed attribute distributions (e.g. the geometric distribution) is thus achieved. The other approach, for variables data, fit a member of a four-parameter family of distributions. However, unlike similar approaches, sample estimates of at most the second degree are employed in the fitting procedure. This has been shown to result in a better representation of the underlying (unknown) distribution than methods based on fourmoment matching. Some numerical comparisons are given.

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Section: Non-normality In Attributes Datamentioning

confidence: 72%

Section: Non-normality In Quality and Reliability Engineering-a Discumentioning

confidence: 98%

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Non‐normal Populations in Quality Applications: a Revisited Perspective

Shore¹

2004

Quality & Reliability Eng

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“…A common practice is to use the Box-Cox transformation for continuous variables and various dedicated transformations for attributes data. Examples of the latter are the arcsin transformation for binomial data or the square root transformation for Poisson data (a recent review of these transformations and others may be found in Shore 3 ). Alternatively, we may opt for the use of GLM.…”

Section: Introductionmentioning

confidence: 98%

Response Modeling Methodology Validating Evidence from Engineering and the Sciences

Shore

2003

Quality & Reliability Eng

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Modeling a response in terms of the factors that affect it is often required in quality applications. While the normal scenario is commonly assumed in such modeling efforts, leading to the application of linear regression analysis, there are cases when the assumptions underlying this scenario are not valid and alternative approaches need to be pursued, like the normalization of the data or generalized linear modeling. Recently, a new response modeling methodology (RMM) has been introduced, which seems to be a natural generalization of various current scientific and engineering mainstream models, where a monotone convex (concave) relationship between the response and the affecting factor (or a linear combination of factors) may be assumed. The purpose of this paper is to provide the quality practitioner with a survey of these models and demonstrate how they can be derived as special cases of the new RMM. A major implication of this survey is that RMM can be considered a valid approach for quality engineering modeling and, thus, may be conveniently applied where theory-based models are not available or the goodness-of-fit of current empirically-derived models is unsatisfactory. A numerical example demonstrates the application of the new RMM to software reliability-growth modeling. The behavior of the new model when the systematic variation vanishes (there is only random variation) is also briefly explored.

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“…Shore [2] developed control chart for random queue length, N of M /M /1queueing model by considering the first three moments. Khaparde and Dhabe [3] constructed the control chart using method of weighted variance for random queue length N for M/M/1queueing model.…”

Section: Introductionmentioning

confidence: 99%

Control chart for waiting time in system of (M / M / S) :( ∞ / FCFS) Queuing model

T¹

2013

IOSR-JM

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General control charts for attributes

Cited by 18 publications

References 15 publications

Non‐normal Populations in Quality Applications: a Revisited Perspective

Non‐normal Populations in Quality Applications: a Revisited Perspective

Response Modeling Methodology Validating Evidence from Engineering and the Sciences

Control chart for waiting time in system of (M / M / S) :( ∞ / FCFS) Queuing model

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