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Statistics education has become increasingly important in today's data-driven world, as the ability to analyze and interpret data is critical in many disciplines. However, introductory statistics courses traditionally emphasize rote calculations and procedural knowledge, which can result in passive learning and disengagement from students who may not see the relevance of statistics to their engineering field. To address these challenges, this paper proposes using Excel worksheets as student learning materials in an introductory statistics course to shift from traditional to experiential learning. Excel worksheets provide a hands-on approach to learning that gives students the experience of the actual process of doing statistics. The Excel worksheet facilitates quick and accurate calculations, allows more time for students to interpret statistical results, and encourages active learning. The Excel worksheet allows for real-world data analysis and what-if analyses, making abstract concepts more accessible. In addition, the Excel worksheets are designed to promote 21st-century thinking and collaboration skills, which are increasingly important in today's workforce. This paper presents several examples of Excel worksheet designs for teaching descriptive statistics, developed using the framework of substitution, augmentation, modification, and redefinition (SAMR) model. Excel worksheets promote deep learning and facilitate students' understanding of statistical ideas, concepts, and methods through learning by doing. The paper concludes that Excel worksheets offer a valuable tool for teaching introductory statistics to engineering students, enhancing their thinking skills, and preparing them for the data-driven demands of their field. [ABSTRACT FROM AUTHOR]

The method of outlier detection with regards to circular regression have been widely developed nowadays. Several diagrammatical plots, numerical presentation as well as hypothesis testing have been recommended in detecting the outliers. As we know, the presence of outliers in dataset significantly impacts the parameter estimation and inference of the statistics. The outlier detection that exists in multiple circular regression model (MCRM) also attracting the interest of statisticians and researchers to do the research in depth. This paper presents the outlier detection method in MCRM using circular distance as well as circular error. The proposed method has been investigated through simulation study and the 5% upper percentiles is considered in obtaining the cut-off point as well as the performance power. Here, the procedure successfully identifies two outliers detected in the data set. [ABSTRACT FROM AUTHOR]

This paper considers outlier detection methods in multiple circular regression. It focuses on the detection of outliers in a multiple circular regression model, where both variables are circular. This model has many interesting properties related to multiple linear regression models. The graphical techniques are investigated to compare the results with the numerical method, where the extension of the COVRATIO statistic in linear regression model is applied to the circular case. In general, both methods are perform well in detecting the outliers in the data set. [ABSTRACT FROM AUTHOR]

This paper studies a discrete event simulation (DES) as a computer based modelling that imitates a real system of pharmacy unit. M/M/c queuing theo is used to model and analyse the characteristic of queuing system at the pharmacy unit of Hospital Tuanku Fauziah, Kangar in Perlis, Malaysia. The input of this model is based on statistical data collected for 20 working days in June 2014. Currently, patient waiting time of pharmacy unit is more than 15 minutes. The actual operation of the pharmacy unit is a mixed queuing server with M/M/2 queuing model where the pharmacist is referred as the server parameters. DES approach and ProModel simulation software is used to simulate the queuing model and to propose the improvement for queuing system at this pharmacy system. Waiting time for each server is analysed and found out that Counter 3 and 4 has the highest waiting time which is 16.98 and 16.73 minutes. Three scenarios; M/M/3, M/M/4 and M/M/5 are simulated and waiting time for actual queuing model and experimental queuing model are compared. The simulation results show that by adding the server (pharmacist), it will reduce patient waiting time to a reasonable improvement. Almost 50% average patient waiting time is reduced when one pharmacist is added to the counter. However, it is not necessary to fully utilize all counters because eventhough M/M/4 and M/M/5 produced more reduction in patient waiting time, but it is ineffective since Counter 5 is rarely used. [ABSTRACT FROM AUTHOR]