Background: COVID-19 was first reported on 31 December 2019 and has so far claimed over 2,000 lives in Nigeria. Through global and national efforts, about 4 million doses of the AstraZeneca vaccine was distributed and used in Nigeria from March 2021. Vaccine hesitancy could pose a serious problem for COVID-19 prevention and control. Objectives: To estimate the proportion of the Nnamdi Azikiwe University community that is willing to be vaccinated against COVID-19; level of hesitancy and its associated factors. Methods: A cross-sectional survey was conducted using online Google form distributed to staff and students of the university via different WhatsApp groups. The outcome measures were the proportion of persons willing to be vaccinated, vaccine hesitancy rates and reasons for this hesitancy. Data were analyzed using SPSS version 23 and Minitab version 19. Bivariate analysis was performed by the chi-square test, Odds Ratios (ORs) and statistical significance was accepted when p-value is < 0.05. Results: Only 349 of the survey responses were analyzed in the survey. Results show that 34.70 ± 5.00% of the university community were willing to receive the COVID-19 vaccine when it is offered to them. The COVID-19 hesitancy rate among staff and students was 65.04 ± 5.00%. It was discovered that marital status (OR = 2.06), age (OR = 0.802) and christian denominational affiliation (OR = 0.366) influenced respondents’ perception of COVID-19 vaccination. Gender, occupation, previous vaccination experience, awareness of COVID-19 and previous symptoms of COVID-19 did not significantly ( p = 0.05) influence respondents’ willingness to be vaccinated. Conclusion: COVID-19 vaccine hesitancy is high among staff and students in a Nigerian university and is significantly influenced by marital status, respondents’ age and christian denominational affiliation.

Many methods are available for the estimation of ridge regression parameter in literature. This paper considered some of these estimators and also proposed some new methods that take care of the skewed eigenvalues of the matrix of explanatory variables. Simulated results obtained indicate that when the sample size increases, the Prediction Sum of Squares (PRESS) value decreases as the value of the correlation coefficient becomes large. One of the proposed methods * 4 K outperforms all the other existing and proposed methods considered in terms of PRESS values.A numerical example with six explanatory variables was used to compare the performance of these estimators.

This study was aimed at analyzing the Nigerian Stock Exchange All Share Index. The data was extracted from the Central Bank of Nigeria's Statistical Bulletin and it covered the period of January 1985 to September 2014. The Box and Jenkins approach of model identification, parameter estimation and diagnostic checking was adopted in the analysis with the aid of S-plus Package. From the analysis, the result revealed that Autoregressive model of order two AR (2) after differencing once gives Akaike Information Criteria (AIC) of 6682.4416 which is an optimal order for Nigeria Stock Exchange All Share Index, the model is 2 1 5123. 0 47. 0     t t t X X X. Therefore, the model generated shows that ARIMA (2, 1, 0) is adequate to define the optimal order of Nigerian Stock Exchange All Share index.

Influential observation is one which either individually or together with several other observations has a demonstrably large impact on the values of various estimates of regression coefficient. It has been suggested by some authors that multicollinearity should be controlled before attempting to measure influence of data point. In using ridge regression to mitigate the effect of multicollinearity, there arises a problem of choosing possible of ridge parameter that guarantees stable regression coefficients in the regression model. This paper seeks to check whether the choice of ridge parameter estimator influences the identified influential data points.

This paper provides estimates of the correlation between genotypic relatives and the effect of allelic recombination on the correlation assuming random mating. It is shown that the correlation is a non negative quantity and that allelic recombination has the effect of reducing total variation and doubling the correlation between genotypic relatives with respect to measurements on the character of interest. The significance of the correlation coefficient as well as the fitted regression model was obtained using Analysis of Variance method.