Parameter estimation and outlier detection for some types of circular model

This study focuses on the parameter estimation and outlier detection for some types of the circular model. We first look at the concentration parameter of von Mises distribution. The von Mises distribution is the most commonly used probability distribution of a circular random variable, and the concentration of a circular data set is measured using the mean resultant length. We propose a new and efficient approximation of the concentration parameter estimates using two approaches, namely, the roots of a polynomial function and minimizing the negative value of the log-likelihood function in this study

SC and IAP Meeting for MQA/FA12425 Program Compliance Assessment Feedbacks

A Study Committee (SC) & Industry Advisory Panel (IAP) Meeting for Feedback on the MQA/FA12425 Program Requirements Compliance Assessment was held on Saturday, February 25, 2023 online

A Benchmark Visit to PSM UMP for The Bachelor of Applied Science In Data Analytics with Honors Programme by The College of Computing, Informatics and Media Studies of Uitm Terengganu Branch, Kuala Terengganu Campus

A benchmark visit to PSM UMP for the Bachelor of Applied Science in Data Analytics with Honors programme by the UiTM College of Computing, Informatics and Media Studies, Terengganu Branch, Kuala Terengganu Campus was held on 22 June 2023, Thursday

Graphical Summaries of Circular Data with Outliers Using Python Programming Language

Graph in statistics is used to summarise and visualise the data in pictorial form. Graphical summary enables us to visualise the data in a more simple and meaningful way so that the interpretation will be easier to understand. The graphical summaries of circular data with outliers is discussed in this study. Most of the time, people use linear data in real life applications. Other than linear data, there is another data type that has a direction which refers to circular data and it is different from linear data in many aspects such as in descriptive statistics and statistical modeling. Unfortunately, the availability of statistical software specialises in analysing circular data is very limited. In this study, the graphical summaries of circular data are plotted using the in-demand programming language, Python. The Python code for generating graphical summaries of circular data such as circular dot plot and rose diagram is proposed. The historical circular data is used to illustrate the graphical summaries with the existence of outliers. This study will be helpful for those who are started exploring circular data and choose Python as an analysis tool

Review on circular-linear regression models

Classical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-linear regression model over the past few decades. It is important to acknowledge these circular data characteristics as it can affect the descriptive and inference of statistical analysis. With the growing body of literature regarding this issue, this paper will review on circular-linear regression model by highlighting and exploring their benefits and limitations

A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language

Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises (VM) distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size, n and five concentration parameters, K. Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are 'numpy', 'random' and von Mises'. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool

Descriptive analysis of extra-curricular program outcome attainment: a case study of Universiti Malaysia Pahang

This study presents the descriptive analysis of students’ performance in extra-curricular activities at Universiti Malaysia Pahang (UMP) for all bachelor degree students from year 2015 to 2018. A new reporting mechanism and formula, known as Extra-Curricular Cumulative Grade Point Average (XCGPA), is proposed for assessing the students’ involvement in extra-curricular program organised by UMP. The data analysed is merit scores obtained by students from joining the extra-curricular activities during their study period in UMP. The merit scores are categorized into involvement, role and achievement with different weightages based on students’ level of involvement. The total merit scores from each category will be mapped between the intended core values and the targeted attributes. There is a threshold merit score for each attribute to be obtained by the student per semester and for the study duration. Then, the percentage score for each attribute is calculated and classified according to six Student Personality Trait Classifications. The students’ profile is developed and the attainment for each attribute is presented by a spider web

Descriptive analysis of students’ CGPA: a case study of Universiti Malaysia Pahang

This study presents the descriptive analysis of students’ performance based on the cumulative grade point average (CGPA) of the entry grade and the CGPA achieved throughout their study duration until graduation. The data analysed are from all bachelor degree program students at Universiti Malaysia Pahang of 2011, 2012 and 2013 cohorts. Components which affect the academic performance are analysed such as gender, entry qualification, entry CGPA, academic program and cohort. The results show that students from Sijil Tinggi Pelajaran Malaysia (STPM) performs very well academically with mean CGPA of 3.30 throughout the study period as compared to students from diploma (CGPA:2.97), matriculation (CGPA:2.88) or Sijil Tinggi Agama Malaysia (STAM; CGPA:2.71). Gender factor do not has significant effect on students’ academic performance

Managing productivity in Universiti Malaysia Pahang: Rethinking the whom, which, what, and whose of productivity

Drawing on reviews of scholarly literature, this study suggests rethinking productivity in Universiti Malaysis Pahang (UMP) along four dimensions: the productivity of whom, productivity for which unit of analysis, productivity according to what functions, and productivity in whose interests. It offers principles for promoting enlightened discussion and pursuit of productivity of academic staff at UMP. In contrast to the dominant discussion, which emphasises focus, centralised standard measures, and accountability, the bias unfairness in this study is toward balance, decentralised diversity, and recalibration. Academic Differentiated Career Pathways (ADCAP) suggest the ideal is not for academic staff and faculties to produce to centrally managed objectives but for all individuals and units faculties to manage individually and collectively to design their work to improve their productivity along multiple dimensions

The effect of different similarity distance measures in detecting outliers using single-linkage clustering algorithm for univariate circular biological data

Clustering algorithms can be used to create an outlier detection procedure in univariate circular data. The circular distance between each point of angular observation in circular data is used to calculate the similarity measure to appropriately group observations. In this paper, we present a clustering-based procedure for detecting outliers in univariate circular biological data using various similarity distance measures. Three circular similarity distance measures; Satari distance, Di distance and Chang-chien distance were used to detect outliers using a single-linkage clustering algorithm. Satari distance and Di distance are two similarity measures that have similar formulas for univariate circular data. This study aims to develop and demonstrate the effectiveness of the proposed clustering-based procedure with various similarity distance measures in detecting outliers. The circular similarity distance of SL-Satari/Di and other similarity measures, including SL-Chang, were compared at various dendrogram cutting points. It is found that a clustering-based procedure using a single-linkage algorithm with various similarity distances is a practical and promising approach to detect outliers in univariate circular data, particularly for biological data. According to the results, the SL-Satari/Di distance outperformed the SL-Chang distance for certain data conditions