Department of Mathematical Sciences
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- ItemA TOPIC-SPECIFIC EVALUATION OF STUDENTS’ ATTITUDES TOWARDS STATISTICS(ICOTS, 2022-12) Ayobami Fadilat AkintolaThis study involves an evaluation of students’ attitudes towards various topics in statistics. The purpose of the study is to determine how students’ attitudes towards statistics vary across different topics and to determine possible changes in students’ attitudes from the beginning to the end of a course. The target population is that of students taking a statistics course for non-majors at a university in Nigeria. The study involved a pre-test (within the first week of the course) and a post-test (applied at the end of the course) focused on specific topics in the statistics course. Results indicated that students’ attitudes were moderately positive at the onset and remained the same at the end of the course for most topics. Implications for teaching statistics are discussed.
- ItemImplementation Strategies and Possible Obstacles to blended learning Design for Statistics Courses.(2023) Ayobami Fadilat AkintolaTechnological advancement and growth has brought about introduction of various methodological dynamics in teaching and learning. A very notable approach in this regard is blended learning, otherwise known as hybrid learning. Although the implementation appears easy in some climes, a purposeful, goal oriented implementation comes with challenges that limit the use and harnessing of its potentials, particularly for statistics courses. In this study, we present a review of blended learning models alongside obstacles to appropriate and successful implementation in statistics course delivery. The study includes a survey of ideas and experiences of statistics lecturers at the university level on challenges and obstacles to blended learning. 75% of the respondents identified with the obstacles listed as affecting blended learning while 87.5% indicated for statistics courses in particular that methods and theories discussed online have to be repeated during in-person classroom lectures. In developing countries, having to repeat classes could be a major setback to any learning process due to challenges with lecturers’ workload. A regression analysis of the blended learning implementation on obstacles to blended learning (general obstacles and obstacles in statistics courses) was carried out. Results suggested that both general obstacles and obstacles in statistics courses significantly influence effective implementation of blended learning design by the respondents (P= 0.000). Specifically, obstacles to blended learning design for statistics courses had a negative effect on the implementation. The literal implication of this result is that, increase in the awareness of the obstacles to blended learning design for statistics courses leads to lower implementation level. In other words, those that mostly agreed that statistics courses have peculiar difficulty in teaching through blended learning designs had low implementation of the design. It indirectly implies that the stated obstacles have been affecting implementation of the design. Attention of stakeholders is drawn to the important issues discussed, so that the benefits of blended learning approach can be maximized for statistics courses.
- ItemA modified generalized class of exponential ratio type estimators in ranked set sampling(scientific African, 2022-11-22) Ayobami Fadilat AkintolaBackground: Researchers consider ranked set sampling (RSS) as an alternative to simple random sampling (SRS) for data collection because studies have shown that it is more efficient and less biased. Also, introducing population parameters to estimators increases the efficiency of such estimators. Aim: This study derived a modified generalized class of exponential ratio estimator in RSS by introducing available population parameters and compared the results with an existing version in SRS. Methodology: The biases and mean square errors (MSE) of the proposed estimators were derived up to the terms of first-order approximation using Taylor’s series expansion. Effi- ciency was used as the mode of comparison between the proposed and existing estimators. Results: Life data sets and simulated data supported the numerical illustration to corrobo- rate the theoretical results. Conclusion: The MSEs of the modified generalized class of estimators under RSS were found to be smaller than those of the existing generalized class of estimators under SRS; hence they are more efficient estimators.
- ItemAir Quality Trends and Pollution Analysis in Nigerian Cities Using Time Series Methods(International Journal of Advanced Statistics and Probability, 2024) Ayobami Fadilat AkintolaAir pollution is a significant environmental and public health issue in rapidly urbanizing cities, particularly in developing countries like Nigeria. This study analyzes air quality trends in five major Nigerian cities Abuja, Lagos, Kano, Port Harcourt, and Enugu using satellitebased remote sensing data from January 2021 to December 2023. Key pollutants, including PM2.5, PM10, CO, NO2, SO2, and O3, were analyzed using time series models (ARIMA, SARIMA), seasonal decomposition (STL), and correlation analysis. The results reveal that Lagos and Kano experience the highest pollution levels, particularly during the Harmattan season, when Saharan dust exacerbates particulate matter. Abuja also sees significant pollution spikes, while Port Harcourt and Enugu show moderate pollution driven by industrial emissions and traffic. The study underscores the need for better air quality monitoring, seasonal interventions, and policies to reduce pollution, particularly during Harmattan
- ItemAn integro-collocation method for determining initial values for ordinary differential equations(corpus intellectual, 2023) Onanaye, Adeniyi SamsonThe collocation method used to formulate an integrated Lanczos Tau method for solving ordinary differential equations with starting values is the subject of this research. In order to uniquely determine the coefficients of the approximant of the solution, an algebraic system of linear equations is created by collocating the perturbed integrated equation at certain equally spaced intervals within the range of integration of the differential equation. The method is used to solve problems involving first- and second order ordinary differential equations, and data collected from numerical analysis supports its correctness and efficacy.