Irfan Aslam

Bio: Irfan Aslam is an academic researcher from National College of Business Administration and Economics. The author has contributed to research in topics: EWMA chart & Estimator. The author has co-authored 3 publications.

Memory Type Ratio and Product Estimators in Stratified Sampling

Abstract: The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

Memory type ratio and product estimators under ranked-based sampling schemes

Abstract: The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators used for estimating the population param...

Policy Review: Academic Cheating in Online Examinations during the COVID-19 Pandemic

Abstract: With the liberalization policies towards the COVID-19 pandemic in various countries, in-person teaching is the mainstream currently. Many countries are more open to their border and quarantine/isolation requirements; however, this does not mean the contagious virus is gone. Various variants are still a threat to people’s health. Online teaching has its essential to students. Our observation was based on the almost three-year pandemic experience towards the widely used online teaching, especially on academic cheating behaviors during examinations. We found that the online teaching during the COVID-19 pandemic facilitated students to obtain high scores through improper cheating in online examinations. Academic faculty faces a big challenge when they try to use new technologies to protect the integrity of online exams because students can develop new strategies for cheating. They not only surf the internet to find the correct answers but also get help from peers and experts. Cheating prevents students from gaining essential skills and knowledge. It is unfair to honest students who spend time and effort studying course materials. In this article, we explored the common ways of cheating on online exams. We also provided recommendations to prevent academic misconduct in the digital environment.

Joint monitoring of mean and variance using Max-EWMA for Weibull process

Abstract: Simultaneously monitoring of process mean and dispersion for the normal process has gained considerable attention. In this manuscript, we have proposed a maximum exponentially weighted moving avera...

Improving Efficiencies of Ratio- and Product-type Estimators for Estimating Population Mean for Time-based Survey

Abstract: Statisticians often use auxiliary information at an estimation stage to increase efficiencies of estimators. In this article, we suggest modified ratio- and product-type estimators utilizing the known value of the coefficient of variation of the auxiliary variable for a time-based survey. Further, to excel the performance of the suggested estimators, we utilize information from the past surveys along with the current surveys through hybrid exponentially weighted average. We obtain expressions for biases and mean square errors of the suggested estimators. The conditions, under which the suggested estimators have less mean square errors than that of other existing estimators, are also obtained. The results obtained through an empirical analysis examine the use of information from past surveys along with current surveys and show that the mean square errors and biases of the suggested estimators are less than that of the existing estimators. For example: for a sample size 5, mean square error and bias of the suggested ratio-type estimator are (0.0414,0.0065) which are less than (0.5581,0.0944) of the existing Cochran (1940) estimator, (0.4788,0.0758), of Sisodia and Dwivedi (1981) estimator and (0.0482,0.0082) of Muhammad Noor-ul-Amin (2020) estimator. Similarly, mean square error and bias of the suggested product- type estimator are (0.0025,−0.0006) which are less than (0.0612,−0.0096) of the existing Murthy (1964) estimator, (0.0286,−0.0071), of Pandey and Dubey (1988) estimator and (0.0053,−0.0008) of Muhammad Noor-ul-Amin (2020) estimator.

ChatGPT: An ever-increasing encroachment of artificial intelligence in online assessment in distance education

Abstract: The use of artificial intelligence (AI) in education is becoming increasingly prevalent, and its encroachment and impact on online education and assessment is a topic of interest to researchers and lecturers. ChatGPT is one such AI model that has been trained on a large corpus of text data to generate human-like responses to questions and prompts. Using the theory of disruptive innovation as a foundation for our argument, this conceptual article explores the potential and disruption of ChatGPT in online assessment. This article also considers the ethical implications of using ChatGPT, particularly in relation to online assessment in distance education. While the use of AI in online assessment presents a myriad of limitations and possibilities, it is crucial to approach its use with caution and consider the ethical implications of academic integrity for online assessment. This article aims to contribute to the ongoing discussion and debate around the use of AI in online higher education and assessment, highlighting the need for continued research and critical evaluation of its impact.

Evaluating the performance of memory type logarithmic estimators using simple random sampling

Abstract: In survey research, various types of estimators have been suggested that consider only the current sample information to compute the unknown population parameters. Therefore, we utilize the past sample information along with the current sample information in the form of hybrid exponentially weighted moving averages to suggest the memory type logarithmic estimators for time-based surveys. The expression of the mean square error of the suggested estimators is determined to the first order of approximation. A relative comparison of the suggested estimators with the existing estimators is performed and efficiency conditions are obtained. Further, a simulation study is accomplished using a hypothetically rendered population and a real data illustration to improve the theoretical results. The results of the simulation study and the real data application exhibit that the consideration of past and current sample information meliorates the efficiency of the suggested estimators.