The global economy seems to be sinking in the ocean of plastic wastes. Plastic circular economy has been prescribed the key panacea with recycling as its key strategy. The issue of sustainable plastic circular economy has so far been the challenge amid the production of virgin plastics. The aim of this study is to assess the sustainability of plastic circular economy with complete dependence on plastic wastes.Sustainability assessment criterion was based on plastic waste generation and recycling transitional probabilities. A closed system is assumed to ensure that no new virgin plastic is produced. Data was obtained from various publications of the Plastic Europe; and other research publications on plastic waste management. Sustainability was then assessed under four scenarios; joint force of plastic waste incineration and discarding, plastic waste discarding without incineration, plastic waste incineration without discarding; and complete riddance of plastic waste incineration and discarding. It was revealed that the already cumulated volume of global annual plastic waste generated can sustain plastic circular economy in a closed system only if plastic waste incineration and discarding are completely prohibited. The study is an indispensable tool for policy formulation in the area of waste management.
Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research was reviewed; next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjectures about the independence of the socio-economic variables and e-commence were tested. Some of the factors influencing patronage of e-commerce were identified. It is clear from the literature that as one's academic qualification improves, there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected patronage in e-commerce. Gender also appeared to affect patronage in e-commerce. 62.3% of staff had patronized e-commerce. This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe. It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.
This study has provided a starting point for defining and working with Cox models in respect of multivariate modeling. In medical researches, there may be situations, where several risk factors potentially affect patient prognosis, howbeit, only one or two might predict patient's predicament. In seeking to find out which of the risk factors contribute the most to the survival times of
This article aimed at examining the determinants of the returns on assets of banks using a panel regression analysis. The explanatory variables considered in the regression model were Capital Adequacy, Assets Quality, Management Quality and Liquidity Ratio. The effects of the explanatory variables on the returns on assets were explored by fitting a panel multiple regression model to the data. The results of the study show that the observed returns across the banks do not change over time, thus indicating that the series is stationary. There is evidence of significant differences across the banks considered for the study, hence the use of Random effects model. All the explanatory variables considered in the study including the intercept were found to have significant effects statistically on the returns on assets across the banks. About 73% of the variations in the returns on assets across the banks can be accounted for by the independent variables.
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