The convolution of Nadarajah-Haghighi-G family of distributions will result into a more flexible distribution (Nadarajah-Haghighi Gompertz distribution) than each of them individually in terms of the estimate of the characteristics in there parameters. The combination was done using Nadarajah-Haghighi (NH) generator. We investigated in the newly developed distribution some basic properties including moment, moment generating function, survival rate function, hazard rate function asymptotic behaviour and estimation of parameters. The proposed model is much more flexible and has a better representation of data than Gompertz distribution and some other model considered. A real data set was used to illustrate the applicability of the new model.
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.
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