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The Poisson regression is popularly used to model count data. However, real data often do not satisfy the assumption of equality of the mean and variance which is an important property of the Poisson distribution. The Poisson – Gamma (Negative binomial) distribution and the recent Conway-Maxwell-Poisson (COM-Poisson) distributions are some of the proposed models for over- and under-dispersion respectively. Nevertheless, the parameterization of the COM-Poisson distribution still remains a major challenge in practice as the location parameter of the original COM-Poisson distribution rarely represents the mean of the distribution. As a result, this paper proposes a new parameterization of the COM-Poisson distribution via the central location (mean) so that more easily-interpretable models and results can be obtained.  The parameterization involves solving nonlinear equations which do not have analytical solutions. The nonlinear equations are solved using the efficient and fast derivative free spectral algorithm. Implementation of the parameterization in R (R Core Team, 2018) is used to present useful numerical results concerning the relationship between the mean of the COM-Poisson distribution and the location parameter in the original COM-Poisson parameterization. The proposed technique is further used to fit COM-Poisson probability models to real life datasets. It was found that obtaining estimates via this parameterization makes the estimation easier and faster compared to directly maximizing the likelihood function of the standard COM-Poisson distribution.

Overweight and obesity which are known to pose serious health problems are becoming increasingly prevalent in Nigeria which is a sub-Saharan African country. This study utilized the 2018 Nigeria Demographic Health Survey to examine demographic and socio-economic risk factors of overweight and obesity among Nigerian women aged 15-49 years. Exploratory analysis was used to provide basic description of the data while a semiparametric structured additive models was used to describe the relationship between the presumed factors and overweight and obesity status while also accounting for spatial effects at state level. The national prevalence of overweight and obesity among Nigerian women was found to be 27.4%. Increased risk of overweight and obesity among Nigerian women was found to be strongly associated with being older, high educational level, being rich, living in an urban area, having many children, being pregnant, and residing in southern part of Nigeria. In respect to ethnicity and religion, the Fulani tribe and Islamic religion were associated with lower prevalence of overweight and obesity. Overweight and obesity were found to be significantly more prevalent in the Southern parts compared to the Northern parts of Nigeria. The highest and lowest prevalence of overweight and obesity were observed in Anambra and Yobe states respectively. Prevalence of overweight and obesity was higher among Muslim women compared to Christian women since most Northern women are Muslims and most Southern women are Christians. Random (unstructured) spatial effects were significant indicating that overweigh/obesity was influenced by unobserved state specific factors

This paper reviews the theory of matrices and determinants. Matrix and determinant are nowadays considered inseparable to some extent, but the determinant was discovered over two centuries before the term matrix was coined. Our review associate determinant with the matrix as part of linear systems but not with polynomials. Thus, the paper first gives the background on matrix with vast applications in all fields of study and then reviews the history of determinants which is based on its major contributors in chronological order from the sixteenth century to the twenty-first century

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