Control chart is a useful technique which helps in detecting out of control signal in a process and it can either be a memory-type or memory-less control chart. This work is focused on evaluating the monthly incidence of diabetic disease using four univariate memory-type control charts. In this study we evaluated the average run length (ARL) properties of the memory-type control charts by adjusting the ARL value’s determinant parameters in each control charts, and the ARL value was set to be 500. The output of the analysis of the data set indicates that the exponential weighted moving average (EWMA) control chart is better in detecting the out of control signal faster in small shifts than its counter parts including CUSUM, MECH and MEC charts. This will help in having adequate plans and prevent the increase in diabetics in the country.
Ajadi, N. (2020). On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data. Afribary. Retrieved from https://tracking.afribary.com/works/on-efficient-memorytype-chart
Ajadi, Nurudeen "On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data" Afribary. Afribary, 01 Jul. 2020, https://tracking.afribary.com/works/on-efficient-memorytype-chart. Accessed 22 Dec. 2024.
Ajadi, Nurudeen . "On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data". Afribary, Afribary, 01 Jul. 2020. Web. 22 Dec. 2024. < https://tracking.afribary.com/works/on-efficient-memorytype-chart >.
Ajadi, Nurudeen . "On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data" Afribary (2020). Accessed December 22, 2024. https://tracking.afribary.com/works/on-efficient-memorytype-chart