DEPARTMENT OF STATISTICS

This research work is aimed at establishing and testing for existing relationship between the Nigerian Gross Domestic Product (GDP) and the indicators of the Nigerian Stock Market. Market capitalization and All Share Price Index are used as proxies for stock market indicators. Annual data set from 1996 to 2024 are used in the research work. The relationship is explored both in the general sense using multiple linear regression analysis and in the period-based (period of global financial crisis and period of no global financial crisis) using
dummy regression analysis. Detailed analysis of the data using the multiple linear regression analysis revealed a strong significant multiple linear relationship among the response and the predictor variables with a coefficient of multiple determination, R2 of about 0.707 which explains about 93% of the total variations in the response variable. The result from the dummy regression analysis shows even a stronger linear relationship among the predictor variables and the response variables with R2 of about 0.93 which explains about 93% of the
total variation in the response variable Y. The coefficient of the dummy variable is significantly different from zero which point to the need to analyse the variables based on the two economic period. It also represent the average decrease in the response variables Y as a result of the global financial crisis given the indicators of the Nigerian stock market.

Background to the Study
National income measures the total value of all goods and services produced in a country in a year. It is the main way to know if an economy is growing or not (World Bank, 2020). For a country like Nigeria, understanding what makes national income rise or fall is very important for planning and improving the lives of its people.

In this study, we explore the power and importance of log-rank statistics in survival analysis.Our research not only enhances our understanding and implementation of thisstatisticalmethod, but also sparks curiosity for future investigations. The applications of log-rankstatistics are vast and diverse, extending beyond disciplinary boundaries andprovidingvaluable insights into the complexities of survival phenomena. Fromlife-savingclinicaltrials to bolstering technological advancements, log-rank statistics have a profoundandfarreaching impact.

Understanding customer’s preference for product features is crucial for businesses seeking to optimize their offerings and increase sales. This study aims to identify key product features that significantly influence purchasing decisions. The research specif ically examines customer preference across different demographic groups, such as age and income levels, to determine variations in product feature importance. Using chi-square test for analysis, this study evaluates the association between demographic factors and product features preferences, providing insight into customer’s decision-making patterns. Based on the analysis it was found that the key product features that affect customers purchasing decision is quality and performance. The findings will help businesses tailor their marketing and product development strategies to better align with costumer expectations

This study discusses the pseudo random number generators, one of the categories used in generating random numbers. Random numbers are useful in various simulation processes, such as statistical and numerical analysis, gaming, cryptography, gambling, etc. It is therefore important to the study the process of generating random numbers. The purpose of this study is to observe the concept of the pseudo-random number generating techniques, some examples and required properties of the pseudo random number generators and a specific pseudo random number generating algorithm for the generation of sequence of random numbers

This research undertakes a comprehensive statistical analysis of Nigeria's Gross Domestic Product (GDP) spanning a decade, with a focus on estimating Autoregressive (AR) models using two prominent statistical methods: the Yule- Walker method and the Least Squares method. The study aims to provide statistical insights into the underlying dynamics of Nigeria's economic performance during this period. The research commences by delineating the statistical framework of AR models, which offer a statistical representation of a time series based on its past values. Subsequently, the Yule-Walker method is introduced, a statistical technique leveraging autocorrelation functions to estimate AR model parameters. The statistical properties of Yule-Walker estimators are elucidated in the context of Nigeria's GDP data. In contrast, the Least Squares method is presented as an alternative statistical
approach, characterized by its objective to minimize the sum of squared prediction errors. A statistical framework for the least squares estimators is outlined, providing insights into the statistical properties of parameter estimates and their significance in explaining variations in Nigeria's GDP. The core of the research involves the statistical analysis of Nigeria's GDP time series data over the 10-year period. Both the Yule-Walker and Least Squares methods are applied to estimate AR models tailored to the GDP data. The statistical comparison is based on goodness-of-fit statistics, such as the Akaike Information Criterion (AIC), to evaluate the models' adequacy in capturing the statistical patterns within the GDP dataset.

This study focused on factorial experiments and its applications in industries. In this study, we examined the application of a two-factor factorial design to determine the significant difference in the mean yield of onion with respect to the effect of fertilizers and plant densities. Primary data (yield of onion) was collected from the Department of Crop science, Faculty of Agriculture, University of Benin. This research work covers only two factors which are fertilizers at three levels (PM, OM and NPK) and plant densities at two levels (LOW and HIGH). The analysis techniques employed was a 2*3 replicated factorial design with 6 replicates per cell. Data collected was analyzed using SPSS version 22. The hypothesis tests were carried out at a (5%) significance level and the decision rule was to reject the null hypothesis if the calculated significance value (p-value) is less than the α (5%). Results from the analyses revealed among others that there is significant difference in the fertilizer effect and plant densities effect on the yield of onion with a significance level of 0.0001 and 0.016 respectively. In addition, there is no significant interaction effect between fertilizers and plant densities with significance value of 0.840 on the yield of onion.