Open Access Short Research Article

Identifying and Predicting Major Factors Affecting the Suicides in Sri Lanka

S. M. M. Lakmali, L. S. Nawarathna

Asian Journal of Probability and Statistics, Page 1-7
DOI: 10.9734/ajpas/2018/v2i328785

Aims: Identifying factors related to suicide and the prediction of future suicides are very important because suicide becomes a significant factor that engaged with education, social status, age, gender and many other factors. Therefore, the main objective of this study is to find the civil and education factors effecting on suicidal attempts in Sri Lanka and propose a model to predict the future suicides.

Study Design: Statistical analysis with descriptive analysis and proposing models for predicting future suicides.

Place and Duration of Study: Data collected from the Department of Police, Sri Lanka, between January 2006 and December 2016.

Methodology: Data set has separated into two categories namely ‘civil data’ and ‘educational data’. We modeled the data from 2006 to 2011 and the data from 2014 to 2016 were used for model validation purposes. Quasi Poisson and negative binomial regression models were fitted to identify the major factors affecting suicide in both categories. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values were used to select the best model. Further, the Mean Absolute Percentage Deviation (MAPD) and Symmetric Mean Absolute Percent Error (SMAPE) were calculated to find the prediction accuracy of the proposed models.

Results: For both regression models, the variables age, gender and level of education are significant for the models fitted for educational data, and civil status and gender are significant in the civil status dataset. According to the analysis, highest suicides were recorded for the age groups 21-30 and over 61 males, minimally-educated and married people. By considering the MAPD values, the prediction accuracy of both Quasi Poisson models and Negative binomial models were above 99%. But the negative binomial model is the best model because of the comparable high accuracy than the other model. A considerable reduction in suicides was obtained in 2010, due to the peaceful situation in Sri Lanka after the civil war. It is observed that by paying special attention to teenagers, old-aged and married people can reduce the number of suicides.

Open Access Original Research Article

Methods for Determining the Tetrachoric Correlation Coefficient for Binary Variables

E. F. El-Hashash, K. M. El-Absy

Asian Journal of Probability and Statistics, Page 1-12
DOI: 10.9734/ajpas/2018/v2i328782

The tetrachoric correlation coefficient (rt) is a special case of the statistical covariation between two variables measured on a dichotomous scale, but assuming an underlying bivariate normal distribution. Our goal was to provide an analysis of seven different methods used to calculate rt. The rt approximation was then used to derive its standard error and its associated confidence interval. Computation of rt is not straightforward and is usually not available in standard statistical packages. This paper introduces seven methods for computing the rt value and three methods used to provide the standard error estimation {SE(rt)}. These methods were illustrated using data from questionnaires that were used to evaluate public awareness regarding Electronic Waste hazards. The different algorithmic/mathematical methods used to estimate rt and SE(rt) yielded values that were equal to (or very close to) each other and the estimates obtained from SAS statistical analysis software. Method 6 and Method 1 used to estimate rt and SE(rt) work very well, the equations are easy to understand, are computationally simple and are ideally suited for use. Additionally, the width of the confidence intervals for these methods are equal to (or closely approximates) the widths calculated by the SAS statistical analysis computer program.

Open Access Original Research Article

Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis

Privatus Christopher

Asian Journal of Probability and Statistics, Page 1-13
DOI: 10.9734/ajpas/2018/v2i328788

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.

Open Access Original Research Article

Analysis of Household Electricity Consumption in Nonresident Rent Halls Using Linear Regression Analysis Model

Omondi S. Odhiambo, Samson W. Wanyonyi, Davis Mwenda Marangu, Irine Jemutai Nguli, Mbuba Morris Mwiti

Asian Journal of Probability and Statistics, Page 1-12
DOI: 10.9734/ajpas/2018/v2i328789

This paper is based on electricity consumption pattern in rental houses around Kibabii University (KU) situated in Western region of Kenya. Because of unexpected blackout faced by nonresident students at the time they need electricity most for their studies, this work intends to find out the directive measure to curb this crisis. Since the usage of electricity showed high relationship to the number of households sharing a common meter, Regression analysis prove to be the most effective method to model a solution to this problem. SPSS was used to analyze the data obtained. The results showed the consistency in linear trend of usage of electrical power on a monthly basis among students, it is observed also that the rate of consumption of power among nonresident students of KU is affected by the number of households sharing the meter. The consequence of this study is that with the correct data in place one is able to know the amount of power in kilowatt-hours needed for consumption throughout the semester and plan effectively so that power loss is not experienced. The results will be so useful to the KPLC (Kenya Power and Lighting Company) and KU fraternity for planning purposes.

Open Access Original Research Article

Analyzing and Forecasting HIV Data Using Hybrid Time Series Models

Liming Xie

Asian Journal of Probability and Statistics, Page 1-12
DOI: 10.9734/ajpas/2018/v2i328793

In real work, we often confront complete linear and nonlinear time series data. But some time series are not pure linear and nonlinear, or complicated one, we need apply two or more models to analyze and predict them. It is necessary to explore and find some novel time series hybrid methods to solve it. Human Immunodeficiency and Virus (HIV) is one of intractable and trouble diseases in the world. Thus, the author of this article wants to analyze and probe into some novel time series methods to get breaking breach in the epidemiology that find some rules in the incidence, distribution, pathogen, and control of HIV in a population.  In this article, to find the best model, auto.arima function is applied to the original time series data to determine autoregressive integrated moving average, ARIMA(0,0,0); ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH), that is, ARIMA-GARCH (1,1) model is used to analyze numbers of people living with HIV for the data of HIV in the world such some important parameters as mu, ar1, ar2, omega, alpha 1, or beta 1 and some specific tests, for example, Jarque-Bera Test, Shapiro-Wilk Test, Ljung-Box Test, etc. Using ARIMA (0, 0, 0) and SARIMA (0,2), seasonal ARIMA, to predict the future values and trends after 2015. Both suggest identical results.