One of the concerns of the air pollution studies is to compute the concentrations of one or more pollutants’ species in space and time in relation to the independent variables, for instance emissions into the atmosphere, meteorological factors and parameters. One of the most significant statistical disciplines developed for the applied sciences and many other disciplines for the last few decades is the extreme value theory (EVT). This study assesses the use of extreme value distributions of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor; Bukit Rambai, Melaka; and Nilai, Negeri Sembilan. Parameters for all distributions are estimated using the Method of Moments (MOM) and Maximum Likelihood Estimator (MLE). Six performance indicators namely; the accuracy measures which include predictive accuracy (PA), coefficient of determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE) are used to find the goodness-of-fit of the distribution. The best distribution is selected based on the highest accuracy measures and the smallest error measures. The results showed that the GEV is the best fit for daily maximum concentration for PM10 for all monitoring stations. The analysis also demonstrates that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 mg/m3 are between ½ and 1½ days.
In today’s digital era, it is possible to use the latest technology to improve student attendance and performance. The purpose of the present study is to determine the relationship between absenteeism and academic performance among Calculus students, as well as to measure the impact of class absence on the student’s final exam scores. Based on this, the use of appropriate
strategy was employed, which is the mobile attendance application to reduce absenteeism among students in higher educational institution. The selection of sample was based on cluster sampling, involving the selection of 87 repeater students. The data collected were analyzed using quartile
regression and independent sample t-test. The result of the findings revealed that the class absence has an impact on the student’s final exam scores. This is because, if the student was absences by 1 class, the final exam score is expected to decrease on average by 1.89%. Hence, findings show that the percentage of absences for the students with manual attendance was higher
than the percentage of absences for the students with mobile attendance application. The application can help to reduce absenteeism by reminding students about recent attendance records.