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プロフィール詳細
プロジェクトを作成
★★★★★
☆☆☆☆☆
Raja Fawad Z.に依頼
Pakistan

I love to play with data. Its my hobby and profession.

プロフィール概要
専門分野
サービス
Research Gray Literature Search, Systematic Literature Review, Secondary Data Collection
Data & AI Statistical Analysis, Data Visualization, Big Data Analytics
職務経験

Lecturer

Sukkur Institute of Business Administration

8月 2017 - 現在

Lecturer (Statistics)

Shaheed Benazir Bhutto University, Shaheed Benazirabad

12月 2015 - 8月 2017

Teaching Fellow

Riphah International University

2月 2014 - 12月 2015

学歴

Mphill Ecionometrics (Statistics and Econometrics)

Pakistan Institute of Development Economics

9月 2012 - 4月 2015

M.Sc. (Statistics)

Quaid-i-Azam University

2月 2009 - 7月 2011

認定資格
出版物
JOURNAL ARTICLE
(2020). Image Interpolation via Gradient Correlation-Based Edge Direction Estimation . Scientific Programming.
(2020). On handling inertia problem of memory charts using break approach . Quality and Reliability Engineering International.
Zafar, R.F., Riaz, M.(2020). On handling inertia problem of memory charts using break approach . Quality and Reliability Engineering International. 36. (5). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1708-1715.
Khan, S., Lee, D.-H., Khan, M.A., Siddiqui, M.F., Zafar, R.F., Memon, K.H., Mujtaba, G.(2020). Image Interpolation via Gradient Correlation-Based Edge Direction Estimation . Scientific Programming. 2020.
Faisal M, Zafar RF, Abbas N, Riaz M, Mahmood T. A modified CUSUM control chart for monitoring industrial processes. Qual Reliab Engng Int. 2018;34:1045–1058. https://doi.org/10.1002/qre.2307 .
Faisal, M., Zafar, R.F., Abbas, N., Riaz, M., Mahmood, T.(2018). A modified CUSUM control chart for monitoring industrial processes . Quality and Reliability Engineering International. 34. (6). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1045-1058.
Raja Fawad Zafar and Tahir Mahmood and Nasir Abbas and Muhammad Riaz and Zawar Hussain(2018). A progressive approach to joint monitoring of process parameters . Computers & Industrial Engineering. 115. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 253 - 268.
Zafar, R.F., Mahmood, T., Abbas, N., Riaz, M., Hussain, Z.(2018). A progressive approach to joint monitoring of process parameters . Computers and Industrial Engineering. 115. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 253-268.
Raja Fawad Zafar and Nasir Abbas and Muhammad Riaz and Zawar Hussain(2014). Progressive Variance Control Charts for Monitoring Process Dispersion . Communications in Statistics - Theory and Methods. 43. (23). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 4893-4907. Taylor & Francis
Zafar, R.F., Abbas, N., Riaz, M., Hussain, Z.(2014). Progressive variance control charts for monitoring process dispersion . Communications in Statistics - Theory and Methods. 43. (23). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 4893-4907.
Abbas, N., Zafar, R.F., Riaz, M., Hussain, Z.(2013). Progressive mean control chart for monitoring process location parameter . Quality and Reliability Engineering International. 29. (3). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 357-367.
Progressive Mean Control Chart for Monitoring Process Location Parameter @article{doi:10.1002/qre.1386, author= {Nasir Abbas and Raja Fawad Zafar and Muhammad Riaz and Zawar Hussain}, title= {Progressive Mean Control Chart for Monitoring Process Location Parameter}, journal= {Quality and Reliability Engineering International}, volume= {29}, number= {3}, pages= {357-367}, keywords= {average run length (ARL), memory control charts, EWMA, CUSUM, progressive mean (PM), statistical process control}, doi= {10.1002/qre.1386}, url= {https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.1386}, eprint= {https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.1386}, abstract= {Control charts are widely used for process monitoring. They show whether the variation is due to common causes or whether some of the variation is due to special causes. To detect large shifts in the process, Shewhart‐type control charts are preferred. Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are generally used to detect small and moderate shifts. Shewhart‐type control charts (without additional tests) use only current information to detect special causes, whereas CUSUM and EWMA control charts also use past information. In this article, we proposed a control chart called progressive mean (PM) control chart, in which a PM is used as a plotting statistic. The proposed chart is designed such that it uses not only the current information but also the past information. Therefore, the proposed chart is a natural competitor for the classical CUSUM, the classical EWMA and some recent modifications of these two charts. The conclusion of this article is that the performance of the proposed PM chart is superior to the compared ones for small and moderate shifts, and its performance for large shifts is better (in terms of the average run length). Copyright © 2012 John Wiley \& Sons, Ltd.}} . Quality and Reliability Engineering International.