Browsing by Author "Abdul Wahab Qamar"

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    Hybrid Double Exponentially Weighted Moving Average (HDEWMA) Control Chart ForInverse Rayleigh Distribution
    (UMT.Lahore, 2021-12-15) Abdul Wahab Qamar
    The Control charts are the most important tool of Statistical Process Control (SPC) tool kit. The “assignable and un-assignable causes” are differentiated via control charts. The objective of the potent process monitoring system is to identify the presence of an “assignable cause”. In this thesis, we have proposed a Hybrid Double Exponentially Weighted Moving Average HDEWMA control chart. The proposed control chart is based on Inverse Rayleigh Distributed lifetimes using simple random sampling (SRS) and ranked set sampling (RSS). Out-of-control-Average Run Length (ARL1) is used to evaluate the performance of the proposed control chart. The HDEWMA control chart is compared with traditional/simple EWMA and CUSUM control charts. The performance of the control chart is evaluated using out of control average run length (ARL1). A real-life example is used to compare the proposed HDEWMA, traditional/simple EWMA chart and CUSUM control chart. It is observed that the proposed HDEWMA control chart outperforms simple EWMA and CUSUM control charts. The HDEWMA control chart can be used for efficient monitoring of the production process in manufacturing industries where the data is coming from inverse Rayleigh Distribution.
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    Hybrid Double Exponentially Weighted Moving Average (HDEWMA) Control Chart ForInverse Rayleigh Distribution
    (UMT.Lahore, 2021-12-15) Abdul Wahab Qamar
    The Control charts are the most important tool of Statistical Process Control (SPC) tool kit. The “assignable and un-assignable causes” are differentiated via control charts. The objective of the potent process monitoring system is to identify the presence of an “assignable cause”. In this thesis, we have proposed a Hybrid Double Exponentially Weighted Moving Average HDEWMA control chart. The proposed control chart is based on Inverse Rayleigh Distributed lifetimes using simple random sampling (SRS) and ranked set sampling (RSS). Out-of-control-Average Run Length (ARL1) is used to evaluate the performance of the proposed control chart. The HDEWMA control chart is compared with traditional/simple EWMA and CUSUM control charts. The performance of the control chart is evaluated using out of control average run length (ARL1). A real-life example is used to compare the proposed HDEWMA, traditional/simple EWMA chart and CUSUM control chart. It is observed that the proposed HDEWMA control chart outperforms simple EWMA and CUSUM control charts. The HDEWMA control chart can be used for efficient monitoring of the production process in manufacturing industries where the data is coming from inverse Rayleigh Distribution.