For Nigeria to attain the goal of becoming one of the twenty (20) largest economies by the year 2030, the life expectancy of her citizens must be robust. This study reveals the relationship that exists between Global Life Expectancies (GLE) and some of its major predictors such as Gross Domestic Products (GDP) per capita, Electricity Consumption (ECM) per capita, and Access to Safe Water (ASW), Infant Mortality Rate (IMR), Maternal Mortality Rate (MMR) and Acquired Immune Deficiency Syndrome (AIDS) reported cases with the aim of formulating an appropriate model for measuring such relationship. Using the Ordinary Least Squares regression analysis, it is observed that the determinants contribute most significantly to the growth of life expectancies. The multiple regression analysis reveals highly significant and negatively linear relationship between life expectancy and maternal mortality rate with a significant and positive association with Gross Domestic Product per capita and access to safe water.
This study investigates the spatial dependence between the poverty rate and various socio-economic indicators in Nigeria. The analysis is based on a dataset comprising unique geographic identifiers and the poverty rate along with other relevant variables. Descriptive statistics reveal that the poverty rate exhibits moderate variability with an average of 4.1240. The correlation analysis shows significant relationships between the poverty rate and household size as well as income level, indicating that larger households and higher incomes are associated with higher and lower poverty rates, respectively. Spatial regression models, including Spatial Autoregressive (SAR), Spatial Error (SEM), Spatial Durbin (SDM), and Spatial Autoregressive Conditional (SAC) models, are employed to explore the spatial dependence. Results indicate the presence of spatial clustering and positive autocorrelation in the poverty rate, as indicated by the Moran's I index with a value of 0.3579 (p-value = 0.0012). However, tests for spatial heteroscedasticity do not reveal significant departures from the assumption of constant error variance. The findings suggest that spatial factors play a crucial role in explaining the poverty rate in Nigeria. The positive spatial autocorrelation indicates the presence of localized poverty clusters, emphasizing the importance of considering spatial effects in policy formulation and targeted interventions. The significant relationships between the poverty rate and household size and income level underscore the need for comprehensive strategies to address these socio-economic indicators for poverty reduction.
In this study, the performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) models was investigated and evaluated using daily confirmed cases of COVID-19 in Nigeria. The stationarity status of the data collected was established using Augmented Dickey Fuller unit root test. The residual normality test was also carried out with the residual plots indicating adequacy of the fitted ARIMA model. The results of neural networks were analyzed using back-propagation for multilayer feed-forward powered by sigmoid function. Utilizing backpropagation method based on three factors expressed in terms of the learning rate, the distance between the actual output and predicted output and the activation function, the network weights were generated The performance indices for ARIMA and ANNs models were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE and the results revealed that the ARIMA model performed better than the ANN model considering the minimum prediction error and forecasting ability. The ARIMA (2, 1, 1) model appeared to be the best fitted model over the ANN model for the daily confirmed covid-19 cases considered.
Purpose: Transportation Problem is a Linear Programming application to physical distribution of goods and services from various origins to several destinations at a minimum cost. Methodology: In this study, five different methods were employed to solve transportation problems arising from unequal demand and supply of goods and variations. The methods considered were in terms of North West Corner Rule, Least Cost Method, Vogel’s Approximation Method, Row Minima Method and Column Minima Method were compared. Unbalanced transportation problems were resolved using Vogel’s Approximation Method (VAM) and Modified Distribution (MODI) methods. Findings: The methods compared produced different results with VAM generating the least transportation cost and better solution. With the MODI method, economic values were generated for the dual variables, uis and vjs associated with the source and demand points respectively.
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