A Recently Formulated Individual Control Chart Designed for Quality Control Applications within the Health-care Domain

Dler H. Kadir; Dlshad M. Saleh; Dashty I. Jamil

doi:10.24086/cuesj.v8n2y2024.pp9-18

Dler H. Kadir Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - Iraq https://orcid.org/0000-0002-1254-721X
Dlshad M. Saleh Department of Accounting and Finance, College of Administration and Economics, Lebanese French University, Kurdistan Region, Iraq https://orcid.org/0009-0001-3213-9205
Dashty I. Jamil Department of Accounting, Cihan University-Erbil, Kurdistan Region, Iraq https://orcid.org/0000-0002-2110-7611

DOI: https://doi.org/10.24086/cuesj.v8n2y2024.pp9-18

Keywords: quality control, individual chart, wavelet process, laboratory testing, deviation

Abstract

Quality control charts are based on data that is also used for various statistical analyses, such as regression, time series analysis, and other types of analyses. This implies that the data used to create these charts may contain outliers, potentially compromising their accuracy. To eliminate any potential noise or pollution from the data, a researcher proposed using wavelet shrinkage with a threshold and comparing the resulting data with Shewart's individual chart. Based on this, a new quality control chart was proposed and built using a universal approach for assessing the level of thresholding, representing the individual chart for wavelet haar with soft and hard thresholding. Following that, a comparison was performed between them as well as with Shewart's individual chart. This letter's most significant conclusion is that, in addition to using two real data points—triglyceride, which was recorded by a laboratory blood analyzer in Al-Jumhuri and at the specialized Center for Cardiology Hospital in Erbil Governorate—it may be possible to use wavelet shrinkage with a threshold to address issues of noise or pollution when Shewart's individual chart was made and used. Moreover, the wavelet method effectively reduces deviation, as demonstrated in both sets of data.

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Author Biographies

Dler H. Kadir, Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - Iraq

Dler H. Kadir is currently a full-time Assistant Professor at the Department of Statistics, College of Administrative and Economics at Salaheddin University-Erbil. He received his Ph.D. degree in the Department of Probability and Statistics at the University of SHeffield in the UK, M.Sc. degree in Statistics from the University of Sulaymaniyah in Iraq, and a B.Sc. Degree in Statistics from Salahaddin University-Erbil. His research interest includes Bayesian inference, MCMC, and Statistical Modeling.

Dlshad M. Saleh, Department of Accounting and Finance, College of Administration and Economics, Lebanese French University, Kurdistan Region, Iraq

Dlshad M. Saleh is a full-time lecturer at Salaheddin University-Erbil's Department of Statistics and Informatics at the College of Administrative and Economics. In 2022 he earned his Ph.D. from the University of Salahaddin's Department of Statistics and Informatics. Hie research interests are: Penalized regression, Discrete Wavelet Transformation, and Quality Control.

Dashty I. Jamil, Department of Accounting, Cihan University-Erbil, Kurdistan Region, Iraq

Dashty I. Jamil is a Lecturer at the Accounting department, cihan university-Erbil, Kurdistan Region, Iraq. He is a member of the council of the College of Administration and Economics. His research interest is statitics.

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