DEPARTMENT OF STATISTICS

This study statistically examined the impact of choice of study programme, gender, age at admission, and ethnic affiliation on students’ academic performance in the University of Benin. The main objective was to determine the extent to which these variables influence students’ academic outcomes. A quantitative research design was adopted, and data were collected from undergraduate students across various faculties using structured questionnaires. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) through descriptive statistics, chi-square test of independence and multiple regression analysis. The results revealed that the first choice of study programme had a significant influence on students’ academic performance, indicating that students who were given their first choice performed better than those who were placed in programmes by external influence. However, the demographic variables—gender, age at admission, and ethnic affiliation—did not show
statistically significant effects on academic performance. This implies that while demographic characteristics may influence students’ experiences, they do not independently determine academic achievement in the University of Benin based on this study. The study concludes that academic performance is more strongly influenced by students’ motivation and programme alignment as encapsulated in their first choice course of study than by demographic differences.

This study investigates the intricate relationship between psychological well-being and academic performance among students at the University of Benin. The research explores the impact of psychological issues such as anxiety, stress, and depression on students' academic journeys, as well as the influence of factors like parental roles and social support on their psychological well-being and academic success. A total of 377 students from various academic programs participated in the study, with statistical analysis, including the Spearman Rank Correlation test, used to extract meaningful insights and Statistical software SPSS used in analyzing data. The Findings drawn from this research are as follows: First, there is a significant correlation between psychological dysfunction, characterized by anxiety, stress, and depression, and students' academic performance. This emphasizes the need to address students' psychological well-being to enhance their academic success. Second, strong parental support plays a crucial role in mitigating psychological dysfunction, positively influencing academic performance. Lastly, fostering social support networks among students contributes to reduced psychological dysfunction and improved academic performance. Based on these insights, the study recommends enhancing psychological support services within the University of Benin, promoting parental engagement programs, and nurturing social support networks among students. These efforts can collectively contribute to students' holistic development, ensuring both their well-being and academic achievements.

In this project work, we look at how the least-square polynomial regression model is used to fit a non-linear relationship between a response variable and an explanatory variable in curve fitting. Finding a mathematical equation or model that best fits a noisy data has experience some drawback in curve fitting. i.e. finding an appropriate fit that best depicts the behaviour of the data. The purpose of this project is to show how the polynomial regression model can be used to show the relationship that exist between two variables; where the linear regression model is inadequate in describing such a relationship. The method of curve fitting used in this study is the least square polynomial regression method. It is designed in a way that, the model parameters are estimated by minimizing the residual term of the polynomial regression model; and then used the model to find the line that best fits the data points of the data set. This method was validated by modelling a data extracted from Nigeria Stock Exchange; and the model was able to predict over 80% of the relationship that existed in the data. It was discovered that the inadequacies of the simple linear regression model in describing the relationship that existed in a data set could be easily tackled by fitting a polynomial regression line. This is done by increasing the power of the independent variable to a higher power until we get a best fit

Lifetime data analysis plays a crucial role in various fields, ranging from engineering to epidemiology. In this study, we investigate the effectiveness of the Paralogistic-Weibull distribution in modeling lifetime data compared to other competing distributions such as the Weibull and Paralogistic distributions. Two datasets were analyzed: the daily number of COVID-19 infected persons in Nigeria and the survival times of patients with Head and Neck Cancer. We employed goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Cramér-von Mises tests, along with discrepancy criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), to evaluate the performance of the distributions. The results indicate that the ParalogisticWeibull distribution consistently outperforms the other distributions across both datasets, exhibiting higher p-values and lower discrepancy criteria values. Therefore, we conclude that the Paralogistic-Weibull distribution offers superior flexibility and accuracy in modeling lifetime data, providing valuable insights for practitioners and researchers in the field of lifetime data analysis.

This study introduces a new distribution known as Marshall-Olkin Lomax-Weibull Distribution, a novel lifetime model that extends the flexibility of an existing Lomax Weibull distribution in reliability analysis and survival studies. The proposed distribution combines the Marshall-Olkin transformation with the Lomax Weibull distributions resulting to an additional scale parameter added to the four parameter Lomax-Weibull Distribution, and enhancing its ability to model diverse hazard rate behaviours, including increasing, decreasing, bathtub, and upside-down bathtub shapes and monotonic and nonmonotonic failure data which could be obtained from complex systems used in diverse scientific fields. Some statistical properties of the proposed lifetime distribution are considered. Parameter estimation of the Marshall-Olkin Lomax-Weibull distribution is obtained using maximum likelihood Estimation. Comparison with other traditional models, applicability and flexibility of the new distribution in lifetime analysis is illustrated with the aid of a real life example. The real world applications demonstrate the superiority of the Marshall-Olkin Lomax Weibull distribution in fitting complex datasets compared to traditional models

In this study, we looked at how we can generate random variables using beta distribution as our target distribution with the acceptance-rejection method. We also used the uniform distribution as our proposal distribution. The inability to invert the CDF is when acceptance-rejection method comes in place. Some simulations were made and results were shown

This study statistically examined the impact of choice of study programme, gender, age at admission, and ethnic affiliation on students’ academic performance in the University of Benin. The main objective was to determine the extent to which these variables influence students’ academic outcomes. A quantitative research design was adopted, and data were collected from undergraduate students across various faculties using structured questionnaires. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) through descriptive statistics, chi-square test of independence and multiple regression analysis.The results revealed that the first choice of study programme had a significant influence on students’ academic performance, indicating that students who were given their first choice performed better than those who were placed in programmes by external influence. However, the demographic variables—gender, age at admission, and ethnic affiliation—did not show statistically significant effects on academic performance. This implies that while demographic characteristics may influence students’ experiences, they do not independently determine academic achievement in the University of Benin based on this study. The study concludes that academic performance is more strongly influenced by students’ motivation and programme alignment as encapsulated in their first choice course of study than by demographic differences.

Several probability models have been introduced in literature to model data sets arising from different real-world scenarios. Such applications are found in the field of engineering, biological sciences, finance, demography, actuarial sciences, agricultural sciences, etc. In this study, we develop a hybrid statistical model which is a composition of the inverse Burr and Topp-Leone distribution. We refer to this model as the inverse Burr Topp-Leone (IBTL) distribution. Basic statistical properties of the IBTL distribution are derived. The method of maximum likelihood estimation is employed to obtain the unknown parameters of the IBTL distribution. A Monte Carlo simulation study is carried out to investigate the asymptotic behavior of the maximum likelihood estimates of the parameters of the IBTL distribution. Finally, two real data sets comprising of the germination rate of oil palm seeds are adapted to illustrate the usefulness of the proposed IBTL distribution in real-life data fittings. For the purpose of model comparison, we considered some existing unit-interval distributions. Result obtained from the analysis revealed that the IBTL distribution outperformed the competitor distributions and thus, becomes a useful alternative to other existing unit-interval distributions in real-life data fittings.

This project work covers the study of the beta distribution which include its moments, the central moments, coefficient of variation, coefficient of skewness and coefficient of kurtosis. An estimation of the rth moment of the beta distribution can be symmetry (asymmetry), negatively skewed (left skewed) or positive skewed (right skewed) at different level of its fixed parameters.

This study investigates the relationships between economic growth, government expenditure, and interest rates in Nigeria, employing various statistical methods. The research aims to provide actionable insights into the interactions between these crucial macroeconomic variables and their implications for policymaking. The theoretical foundation draws from Wagner's Law and the Keynesian Framework, which offer contrasting perspectives on whether government expenditure is a cause or effect of economic growth. Using data from the Central Bank of Nigeria and the World Bank, the study employs the Augmented Dickey- Fuller (ADF) unit root test to assess stationarity, the Granger causality test to examine causal relationships, and Ordinary Least Squares (OLS) regression to analyze the effects of interest rates and government expenditure on Gross Domestic Product (GDP). The findings confirm the applicability of Wagner's Law in the Nigerian context, indicating that economic growth granger-causes government spending. Furthermore, the analysis reveals a positive relationship between interest rates, government expenditure, and GDP, although the results are not statistically significant. The study highlights the importance of interest rates as a policy instrument for influencing economic performance and attracting foreign investment. To enhance the statistical robustness of the analysis, the study incorporates the Analysis of Variance (ANOVA) table, demonstrating its effectiveness in x evaluating and improving the performance of regression models. The research culminates in actionable recommendations for policymakers, emphasizing the need for strategic fiscal policies, careful interest rate management, and targeted investments in sectors that foster economic growth. Overall, this study contributes to the understanding of the intricate dynamics between economic growth, government expenditure, and interest rates in Nigeria, providing valuable insights for policymakers and researchers alike.