This article investigates the strength and limitation of t-test and

In this study, a deterministic co-infection model of dengue virus and malaria fever is proposed. The disease free equilibrium point (DFEP) and the Basic Reproduction Number is derived using the next generation matrix method. Local and global stability of DFEP is analyzed. The result show that the DFEP is locally stable if R0dm < 1 but may not be asymptotically stable. The value of R0dm calculated is 19.70 greater than unity; this implies that dengue virus and malaria fever are endemic in the region. To identify the dominant parameter for the spread and control of the diseases and their co-infection, sensitivity analysis is investigated. From the numerical simulation, increase in the rate of recovery for co-infected individual contributes greatly in reducing dengue and malaria infections in the region. Decreasing either dengue or malaria contact rate also play a significant role in controlling the co-infection of dengue and malaria in the population. Therefore, the center for disease control and policy makers are expected to set out preventive measures in reducing the spread of both diseases and increase the approach of recovery for the co-infected individuals.

No abstract

Accurate prediction of the natural gas consumption in Nigeria is crucial to Gas management. This study utilizes the improved Grey model (MGM(1,1,⊗b)), which is an improvement of the modified Grey model (MGM(1,1)), to forecast the natural gas consumption of Nigeria for the year 2021 to 2025. A secondary data retrieved from the NNPC 2019 annual statistics bulletin was used to build a model for this prediction. Noting that MGM(1,1) model uses the Grey action quantity as a unique real number which do not reflect the uncertainty nature of Grey systems. A model (MGM(1,1,⊗b)) was developed such that it extends the MGM(1,1) model to retain the uncertainty nature of Grey systems. The new modified Grey model (MGM(1,1,⊗b)) was used to make prediction of the natural gas consumption of Nigeria and the results shows that the (MGM(1,1,⊗b)) model gives a prediction interval which the actual value is bracketed. This implies that natural gas consumption of Nigeria for 2021 to 2025 lies within the (MGM(1,1,⊗b)) model prediction values for the same year.

Linear regression is the measure of relationship between two or more variables known as dependent and independent variables. Classical least squares method for estimating regression models consist of minimising the sum of the squared residuals. Among the assumptions of Ordinary least squares method (OLS) is that there is no correlations (multicollinearity) between the independent variables. Violation of this assumptions arises most often in regression analysis and can lead to inefficiency of the least square method. This study, therefore, determined the efficient estimator between Least Absolute Deviation (LAD) and Weighted Least Square (WLS) in multiple linear regression models at different levels of multicollinearity in the explanatory variables. Simulation techniques were conducted using R Statistical software, to investigate the performance of the two estimators under violation of assumptions of lack of multicollinearity. Their performances were compared at different sample sizes. Finite properties of estimators’ criteria namely, mean absolute error, absolute bias and mean squared error were used for comparing the methods. The best estimator was selected based on minimum value of these criteria at a specified level of multicollinearity and sample size. The results showed that, LAD was the best at different levels of multicollinearity and was recommended as alternative to OLS under this condition. The performances of the two estimators decreased when the levels of multicollinearity was increased.