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The purpose of the study would typically involve investigating how the ongoing COVID-19 pandemic has impacted the learning abilities of university students. The study could encompass various factors that may influence students' ability to learn, such as their academic performance, motivation, engagement, mental health, access to resources and learning environment.

1. A cross-sectional design, collecting data at a specific point in time to capture the impact of the COVID-19 pandemic on students' learning abilities. 2. A structured questionnaire will be developed based on relevant literature and research objectives. 3. The questionnaire could be administered to the targeted sample of university students through online surveys, email or other appropriate methods. 4. Descriptive statistics could be used to summarize the demographic characteristics and other relevant variables of the sample. Factor analysis could be performed to identify underlying factors. The Mann–Whitney test, a non-parametric test, could be used to compare the differences between groups.

The results of the data analysis would be interpreted and discussed in the context of the research objectives and relevant literature. The findings could provide insights into the factors that significantly affect learning abilities during the COVID-19 pandemic and highlight any differences among groups. Limitations of the study, such as sample size or potential biases, would also be discussed.

The study would provide a comprehensive overview of the impact of the COVID-19 pandemic on university students' learning abilities, highlight the existing literature in the field and establish the need for further research to investigate the factors influencing students' learning abilities during the pandemic. The study can provide a solid framework for similar studies.

This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart.

The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, μ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, σ. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart.

The proposed robust X¯_MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non‐normal. It has good properties for heavy‐tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart.

The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.