This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the College of Information and Communication Technology Nueva Ecija University of Science and Technology. The...This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the College of Information and Communication Technology Nueva Ecija University of Science and Technology. The system made the OJT course procedure trouble-free by emerging a system accessible through the internet. Students have a user account, which gives them the aptitude to upload document files of their reports, thereby minimizing the time and energy spent traveling from the company’s location to the university and the other way around. Similarly, the OJT coordinators of the college are given their accounts to access and check the reports submitted by the students. The system is capable of generating reports and requirements in real-time, as long as all data is stored within the database and, therefore, the process is completed online. In addition, the system provides an interactive website that might help both students and coordinators to communicate instantaneously by having an online help desk where the students can ask related questions on their OJT course that the OJT coordinator and other students will answer. The coordinators can send a brief message service to the students enrolled within the OJT course through the utilization of the proposed system - this can be for the students who aren’t capable of opening their account more often, in order that they are still informed of the announcements they need to understand immediately. The interactive OJT help desk system with SMS can be used as a tool to help the students of the College of Information and Communication Technology (CICT) and the OJT coordinators in their tasks more conveniently.展开更多
Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be ...Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be the lack of dissemination and further exploration of this new measure. Additionally, the authors focused mainly on specific probability distributions. One of the advantages of AS is that it considers the entire shape of the distribution, rather than focusing only on moments or the linear distance between central tendency statistics or quantiles. This holistic approach makes AS particularly robust in cases where the distribution deviates from normality or contains outliers. This paper aims to generalize its use to random samples with either known or unknown distributions. The study has three objectives: 1) to develop an R script for point and interval estimation of AS;2) to provide interpretive norms of normality by examining normality in bootstrap sampling distributions;and 3) to compare asymptotic and bootstrap standard errors. Interval estimation is approached asymptotically and through bootstrap. The script was illustrated using two examples: one with generated data and another with real-world data. Interpretive norms of normality are derived from 40 samples of various sizes, created by inverse transform sampling to follow a standard normal distribution. Bootstrap intervals at three confidence levels (0.9, 0.95, and 0.99) were obtained using the normal method, with two exceptions: the bias-corrected and accelerated percentile method for the 60-data sample and the percentile method for the 600-data sample, as these deviated from normality. Asymptotic 95% confidence intervals are also provided. The asymptotic standard error was larger than the bootstrap one, with the difference decreasing as the sample size increased. The script is concluded to have practical and educational utility for estimating AS, whose asymptotic sampling distribution is normal.展开更多
文摘This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the College of Information and Communication Technology Nueva Ecija University of Science and Technology. The system made the OJT course procedure trouble-free by emerging a system accessible through the internet. Students have a user account, which gives them the aptitude to upload document files of their reports, thereby minimizing the time and energy spent traveling from the company’s location to the university and the other way around. Similarly, the OJT coordinators of the college are given their accounts to access and check the reports submitted by the students. The system is capable of generating reports and requirements in real-time, as long as all data is stored within the database and, therefore, the process is completed online. In addition, the system provides an interactive website that might help both students and coordinators to communicate instantaneously by having an online help desk where the students can ask related questions on their OJT course that the OJT coordinator and other students will answer. The coordinators can send a brief message service to the students enrolled within the OJT course through the utilization of the proposed system - this can be for the students who aren’t capable of opening their account more often, in order that they are still informed of the announcements they need to understand immediately. The interactive OJT help desk system with SMS can be used as a tool to help the students of the College of Information and Communication Technology (CICT) and the OJT coordinators in their tasks more conveniently.
文摘Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice. One reason for this may be the lack of dissemination and further exploration of this new measure. Additionally, the authors focused mainly on specific probability distributions. One of the advantages of AS is that it considers the entire shape of the distribution, rather than focusing only on moments or the linear distance between central tendency statistics or quantiles. This holistic approach makes AS particularly robust in cases where the distribution deviates from normality or contains outliers. This paper aims to generalize its use to random samples with either known or unknown distributions. The study has three objectives: 1) to develop an R script for point and interval estimation of AS;2) to provide interpretive norms of normality by examining normality in bootstrap sampling distributions;and 3) to compare asymptotic and bootstrap standard errors. Interval estimation is approached asymptotically and through bootstrap. The script was illustrated using two examples: one with generated data and another with real-world data. Interpretive norms of normality are derived from 40 samples of various sizes, created by inverse transform sampling to follow a standard normal distribution. Bootstrap intervals at three confidence levels (0.9, 0.95, and 0.99) were obtained using the normal method, with two exceptions: the bias-corrected and accelerated percentile method for the 60-data sample and the percentile method for the 600-data sample, as these deviated from normality. Asymptotic 95% confidence intervals are also provided. The asymptotic standard error was larger than the bootstrap one, with the difference decreasing as the sample size increased. The script is concluded to have practical and educational utility for estimating AS, whose asymptotic sampling distribution is normal.
