@article{MAKHILLJEAS202015619189,
    title = {Enhancing R Control Chart Performance in Monitoring Process Dispersion using
Scaled Weighted Variance Method for Skewed Populations},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {6},
    pages = {1508-1514},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.1508.1514},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.1508.1514},
    author = {Abdu,Majed,Sharipah,Ali and},
    keywords = {R control chart,skewed population,scaled weighted variance method,SWV-R,WV-R,SC-R},
    abstract = {This study improves the performance of R control chart for monitoring process dispersion of skewed
populations using scaled weighted variance method. This control chart, called Scaled Weighted Variance R
control chart (SWV-R) hereafter, the SWV-R control chart compared with Skewness Correction R chart (SC-R)
and Weighted Variance R chart (WV-R) in terms of false alarm. In terms of probability of detection rates the
proposed SWV-R chart is compared with R chart of the exact method, SC-R and WV-R control charts. The
proposed SWV-R control chart reduces to the Shewhart R control chart when the underlying distribution is
symmetric. An illustrative example is given to show how the proposed SWV-R control chart is constructed and
works simulations study show that the proposed SWV-R control chart has the lower false alarm rates than the
SC-R and WV-R control charts, when the underlying distributions are Weibull and gamma. In terms of the
probability of detection rates, the proposed SWV-R control chart is closer to R control chart with the exact
method than WV-R and almost the same performance as SC-R chart.}
    }