This data article contains the partial analysis (descriptive statistics) of data obtained from 1770 primary school pupils and secondary school students in three Local Government Areas of Ogun State, Nigeria. The schools are either privately owned or public (government owned) schools. The aim of the field survey is to measure the level and patterns of externalizing behavior of the respondents. The data was collected using a standardized questionnaire. The questionnaire is a modification of Achenbach manual for Child behavior checklist (Achenbach, 2001) [1] and manual for Youth self-report (Achenbach and Rescorla, 2001) [2]. The questionnaire was designed to suit the demographic and socio-cultural nature of the target population. Analysis of the data can provide useful insights to the patterns of externalizing behavior of primary school pupils and secondary school students.
Developing new compound distributions which are more flexible than the existing distributions have become the new trend in distribution theory. In this present study, the Lomax distribution was extended using the Gompertz family of distribution, its resulting densities and statistical properties were carefully derived, and the method of maximum likelihood estimation was proposed in estimating the model parameters. A simulation study to assess the performance of the parameters of Gompertz Lomax distribution was provided and an application to real life data was provided to assess the potentials of the newly derived distribution. Excerpt from the analysis indicates that the Gompertz Lomax distribution performed better than the Beta Lomax distribution, Weibull Lomax distribution, and Kumaraswamy Lomax distribution.
BACKGROUND: Noise pollution has become a major environmental problem leading to nuisances and health issues. AIM: This paper aims to study and analyse the noise pollution levels in major areas in Ota metropolis. A probability model which is capable of predicting the noise pollution level is also determined. METHODS: Datasets on the noise pollution level in 41 locations across Ota metropolis were used in this research. The datasets were collected thrice per day; morning, afternoon and evening. Descriptive statistics were performed, and analysis of variance was also conducted using Minitab version 17.0 software. Easy fit software was however used to select the appropriate probability model that would best describe the dataset. RESULTS: The noise levels are way far from the WHO recommendations. Also, there is no significant difference in the effects of the noise pollution level for all the times of the day considered. The log-logistic distribution provides the best fit to the dataset based on the Kolmogorov Smirnov goodness of fit test. CONCLUSION: The fitted probability model can help in the prediction of noise pollution and act as a yardstick in the reduction of noise pollution, thereby improving the public health of the populace.
The data in this article contains statistical analysis of radioelement in Odo-Oba flood plain of crystalline bedrock, Southwestern Nigeria. The data were acquired along twenty-two traverses. The length of each traverse is a function of its accessibility in the study area. The traverses covered the area used for agricultural practices and the area where these farm products are being sold to either the retailers or the consumers. Descriptive and multivariate statistical analyses were used to explore the measured emitted gamma radiation in Odo-Oba flood plain. The dataset can provide insights into the risks involved in exposure to outdoor radiation in a commercial centre when the average outdoor gamma radiation levels are compared to the global threshold values from the regulatory bodies such as World Health Organization, National Research Council, United States Environmental Protection Agency, Federal Environmental Protection Agency, International Commission on Radiological Protection, the United Nations Scientific Committee on the Effects of Atomic Radiation, and Federal Radiation Protection Service among others.
In this data article, records on demographic data, family problem issues, as well as results of medical tests from five major classes of psychotic disorder namely: bipolar; vascular dementia, minimal brain dysfunction; insomnia; and schizophrenia, were collected on 500 psychotic patients carefully selected from the pool of medical records of Yaba Psychiatric Hospital, Lagos, Nigeria, for the period of 5 years, between January 2010 and December 2014, were examined. X-squared Statistic was used to examine each of psychotic disorders to identify demographic (age, gender, religion, marital status, and occupation) and family issues (loss of parent, history of such ailment in the family (family status), divorce, head injury, and heredity of such ailment (genetic) factors that influence them. A clear description on each of these psychotic disorders (bipolar; vascular dementia, minimal brain dysfunction (MBD), insomnia and Schizophrenia) was considered separately using tables and bar diagrams. Data analysis results are as follows: firstly, 40.2%, of the 500 psychotic patients tested positive to bipolar, 40.6% to insomnia, 75.0% to schizophrenia, 43.6% to MBD and 69.2% to vascular dementia. Secondly, female patients were more prone to all the psychotic indicators than their male counterpart except in MBD. Thirdly, the oldest age group (> 60 years) is more prone to bipolar and insomnia ailments, while the mid age group (30 – 60 years) is prone to schizophrenia and vascular dementia, and the youngest group (< 30 years) is prone to MBD. Lastly, the factors that influence the ailments are listed: bipolar (age, occupation, marital status, divorce, and spiritual consultation); insomnia (age, occupation, marital status, divorce, and spiritual consultation); schizophrenia (age, occupation, religion, marital status, hereditary, and divorce); MBD (gender, age, occupation, and marital status); and vascular dementia (history of the ailment and spiritual consultation). Bipolar and insomnia are influenced by the same set of factors, which implies that any patient having one is most likely to be at risk of having the other.
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