Evidence map›Paper›PMID 38866892›Full record

ArticleScientific reports2024

An enhanced nonparametric quality control chart with application related to industrial process.

Muhammad Abid, Mei Sun, Aroosa Shabbir, M E Bakr, Tahir Abbas

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muhammad AbidSchool of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, People's Republic of China. mabid@gcuf.edu.pk.
Mei SunSchool of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, People's Republic of China. sunm@ujs.edu.cn.
Aroosa ShabbirDepartment of Statistics, Government College University, Faisalabad, 38000, Pakistan.
M E BakrDepartment of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
Tahir AbbasDepartment of Mathematics, College of Sciences, University of Sharjah, Sharjah, UAE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In various practical situations, the information about the process distribution is sometimes partially or completely unavailable. In these instances, practitioners prefer to use nonparametric charts as they don't restrict the assumption of normality or specific distribution. In this current article, a nonparametric double homogeneously weighted moving average control chart based on the Wilcoxon signed-rank statistic is developed for monitoring the location parameter of the process. The run-length profiles of the newly developed chart are obtained by using Monte Carlo simulations. Comparisons are made based on various performance metrics of run-length distribution among proposed and existing nonparametric counterparts charts. The extra quadratic loss is used to evaluate the overall performance of the proposed and existing charts. The newly developed scheme showed comparatively better results than its existing counterparts. For practical implementation of the suggested scheme, the real-world dataset related to the inside diameter of the automobile piston rings is also used.

Indexed as

Average run lengthControl chartDouble Homogeneously weighted moving averageNonparametricNormalityRankedWilcoxon signed-rank test

Identifiers

PMID38866892
PMCPMC11169497

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.