Open Access Original Research Article

Measuring the Caesarean Risk Factors in Bangladesh by Using Binary Logistic Regression Model

Sabrina Rahaman, Md. Murad Hossain, Fahima Akter Ankhi, Madhusudan Roy

Asian Journal of Probability and Statistics, Page 1-11
DOI: 10.9734/ajpas/2019/v3i330094

Caesarean section (CS) has been on the rise worldwide and Bangladesh is no exception. In Bangladesh, the CS rate, which includes both institutional and community-based deliveries, has increased from about 3% in 2000 to about 24% in 2014. Rather than numerous impediments, cesarean conveyances are most basic among woman’s however it is not clinically advocated. For enhancing the maternal wellbeing status, it is basic to decide the risk components of cesarean conveyance. The primary focal point of this examination is to research and recognize the cesarean risk factors in the entire territory of Bangladesh. For this investigation, we have gathered auxiliary information from the Bangladesh Demographic and Health Survey (BDHS) 2014. This dataset has one record for every eligible woman as defined by the household schedule. It contains 17864 data which is collected in the woman's questionnaire plus some variables from the household. For the examination, a chi-square test was performed to identify the significant association between conveyance type (cesarean/non-cesarean) and socio-demographic and financial factor's individual. A binary logistic regression was completed to recognize the most effective factors on cesarean conveyance. We found that 5 factors (i.e respondent age, respondent highest education level, husband’s occupation, type of place of residence, wealth index) were measurably connected with conveyance type out of 13 chance elements. From this investigation, it is obvious to us that the above powerful factors may influences the mother's wellbeing status in Bangladesh.

Open Access Original Research Article

Maximum Vanishing Moment of Compactly Supported B-spline Wavelets

Kanchan Lata Gupta, B. Kunwar, V. K. Singh

Asian Journal of Probability and Statistics, Page 1-8
DOI: 10.9734/ajpas/2019/v3i330095

Spline function is of very great interest in field of wavelets due to its compactness and smoothness property. As splines have specific formulae in both time and frequency domain, it greatly facilitates their manipulation. We have given a simple procedure to generate compactly supported orthogonal scaling function for higher order B-splines in our previous work. Here we determine the maximum vanishing moments of the formed spline wavelet as established by the new refinable function using sum rule order method.

Open Access Original Research Article

Statistical Monitoring of Tobacco Moisture Using the Particle Size Distribution Data-a Multidimensional Scaling Audit

Chisimkwuo John, Chukwuemeka O. Omekara

Asian Journal of Probability and Statistics, Page 1-13
DOI: 10.9734/ajpas/2019/v3i330096

Tobacco manufacturers see the tobacco moisture content as one of the determining factors in the quality of the finished tobacco product. During primary processing stage, the Particle Size Distribution (PSD) of the cut tobacco is a good measure of the tobacco moisture content. This paper presents statistical analyses of a two month PSD data using graphical techniques from noteworthy statistical multidimensional scaling (MDS) approaches in characterizing the tobacco moisture quality ratio. At the end, the evaluation within the investigated months fosters an indicative process audit, control and predictive monitoring that is capable of providing valuable impacts to future production.

Open Access Original Research Article

Comparison of Models Used to Predict Flight Delays at Jomo Kenyatta International Airport

P. K. Gachoki, M. M. Muraya

Asian Journal of Probability and Statistics, Page 1-8
DOI: 10.9734/ajpas/2019/v3i330097

Delays in flights have negative socio-economics effects on passengers, airlines and airports, resulting to huge economic loses. Therefore, their prediction is crucial during the decision-making process for all players of aviation industry for proper management. The development of accurate prediction models for flight delays depend on the complexity of air transport system and airport infrastructure, hence may be country specific. However, there exists no prediction models tailored to Kenyan aviation industry. Hence there is need to develop prediction models amenable to Kenya aviation conditions. The objective of this study was to compare the prediction power of the developed models. Secondary data from Jomo Kenya International Airport (JKIA) was used in this study. The data collected included the day of the flight (Monday to Sunday), the month (January to December), the airline, the flight class (domestic or international), season (summer or winter), capacity of the aircraft, flight ID (tail number) and whether the flight had flown at night or during the day. The analysis of the data was done using R- software. Three models, Logistic model, Support Vector Machine model and Random Forest model, were fitted. The strength and utility of the models was determined using bias-variance learning curves. The study revealed that the models predicted delays with different accuracies. The Random Forest model had a prediction accuracy of 68.99% while the Support Vector Machine model (SVM) had an accuracy of 68.62% and the Logistic Regression model had an accuracy of 66.18%. The Random Forest model outperformed the SVM and Logistic Regression with accuracies of 0.37% and 2.71% respectively. The SVM and Random Forest do not assume probability distribution of the response under investigation, probably indicating why they performed better than the logistic regression. The study recommends application of Random Forest model to predict flight delays at JKIA.

Open Access Review Article

Comparative Study on the Degree of Randomness of Few Popular Random Number Tables

Pramit Pandit, Bishvajit Bakshi

Asian Journal of Probability and Statistics, Page 1-8
DOI: 10.9734/ajpas/2019/v3i330093

In the field of statistics as well as in the different branches of experimental sciences, random number tables have been playing a vital role for the purpose of selecting random samples. Among the existing different random number tables, four tables namely, Tippet’s random number table, Fisher and Yates random number table, Kendall and Smith's random number table and random number table of RAND Corporation are of most frequent use. The current study aims at attempting to make a comparative review on the degree on randomness of these four most frequently used random number tables based on  test, run test and deviation test. From the findings based on  test, the highest degree of randomness has been observed in random number table due to RAND Corporation followed by due to Kendall and Smith, Tippet, Fisher and Yates, respectively. In case of run test, the highest degree of randomness has been noticed in random number table due to Fisher and Yates followed by due to Tippet, RAND Corporation, Kendall and Smith, respectively. However, from the findings based on the deviation test, the highest degree of randomness has been observed in random number table due to Kendall and Smith followed by due to Fisher and Yates, RAND Corporation, Tippet, respectively. It can observed that the findings obtained in the studies based on different tests are not alike. Consequently, there is necessity to search for the reasons of the difference between these findings. Moreover, it can also be concluded that attempts should be made by the researchers to construct new random numbers table with enhanced degree of randomness than that of the existing tables.