Statistical Quality Control: Classical, Memory-Type, Multivariate, and Bayesian Control Charts with Simulation and Realistic Data Applications
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2025
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| _version_ | 1866901411532374016 |
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| author | Hayawi, A. A. Hayawi |
| author_facet | Hayawi, A. A. Hayawi |
| contents | <p><span>Statistical Quality Control (SQC) provides a rigorous statistical framework for monitoring production and service processes. This paper presents a comprehensive treatment of classical Shewhart control charts, attribute charts, memory-type charts (EWMA and CUSUM), multivariate control charts, and Bayesian control charts. Detailed mathematical formulations with numbered equations are provided. A Monte Carlo simulation study evaluates Average Run Length (ARL) performance. Finally, realistic synthetic datasets are analyzed to demonstrate practical implementation.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18077984 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Statistical Quality Control: Classical, Memory-Type, Multivariate, and Bayesian Control Charts with Simulation and Realistic Data Applications Hayawi, A. A. Hayawi <p><span>Statistical Quality Control (SQC) provides a rigorous statistical framework for monitoring production and service processes. This paper presents a comprehensive treatment of classical Shewhart control charts, attribute charts, memory-type charts (EWMA and CUSUM), multivariate control charts, and Bayesian control charts. Detailed mathematical formulations with numbered equations are provided. A Monte Carlo simulation study evaluates Average Run Length (ARL) performance. Finally, realistic synthetic datasets are analyzed to demonstrate practical implementation.</span></p> |
| title | Statistical Quality Control: Classical, Memory-Type, Multivariate, and Bayesian Control Charts with Simulation and Realistic Data Applications |
| url | https://doi.org/10.5281/zenodo.18077984 |