Browsing by Author "Adeleke, M. O."
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Item A Comparison of some Discriminant Techniques in Predicting Blood Pressure(Faculty of Science, University of Ibadan, Nigeria, 2019) Yussuf, T; Adeleke, B. L.; Oyeyemi, G. M.; Adeleke, M. O.; Kareem, A. O.The paper compared the performance of the Logistic Regression (LR), Fishers Discriminant Analysis (FDA) and the Support Vector Machines (SVM) in predicting high blood pressure. The variables used in the study are Age, Gender, BMI, Cholesterol and the smoking status. The SVM model was tuned to get the best parameter combination and cost function to avoid over fitting and under fitting of the model. The tuning model was used to compare the performance of the SVM model for sample sizes of 100, 500, 5000 and 7900. This approach was carried out for both the LR and FDA. The Data was divided into train and test data sets in the ratio 80:20 for all sample sizes considered to test the performance of the fitted models. The results from the sample sizes considered showed that for sample size of 100, the FDA performed better than the LR and SVM. But for sample sizes of 500, 5000 and 7900, the SVM performed better than both the LR and LDA. The area under the receiver operating curve showed 81.6% for the test data. This means that about 81.6% of the dataset was correctly predicted. The confusion matrix for the three approaches was computed. The result obtained showed the superiority of SVM to the other two methods.Item Comparisons of Some Outlier Detection Methods in Linear Regression Model(Faculty of Physical Sciences, University of Ilorin, Nigeria, 2017) Oyeyemi, G. M.; Oluwaseun, O. B.; Adeleke, M. O.Empirical evidence suggests unusual or outlying observations in data sets are much more prevalent than one might expect and therefore this paper addresses multiple outliers in linear regression model. Although reliable for a single or a few outliers, standard diagnostic techniques from an Ordinary Least Squares (OLS) fit can fail to identify multiple outliers. The parameter estimates, diagnostic quantities and model inferences from the contaminated data set can be significantly different from those obtained with the clean data. A regression outlier is an observation that has an unusual value of the dependent variable Y, conditional on its value of the independent variable X. Four procedures for detecting outliers in linear regression were compared; the Cook’s, DFFITS, DFBETAS, and Mahalanobi’s distances. DFBETAS is most efficient in outlier detection for small sample and small percentage of outliers but has low sensitivity when the sample size is large. Mahalanobi has more power of detection of small percentage of outliers regardless of sample size.Item Dual Statistical Quality Control Charts with Table of Quality Determinant in Manufacturing Industries(International Journal of Information Processing and Communication (IJIPC), 2019-05) Saka, A.J.; Izekor, J.A.; Akeyede, I; Adeleke, M. O.; Adeleke, B. L.Statistical Quality Control (SQC) techniques have been widely recognized as effective approaches for monitoring both manufacturing and service processes with respect to the use of either variable or attribute charts in a particular process. In this study, both variable and attribute charts were employed to evaluate the performance of a manufacturing process. The variables under study are the original gravity, CO2, PH, dissolved oxygen, real extract, pasteurized unit, colour, alcohol and temperature. The variable chart, or rather X bar chart), was employed, to obtain the Table of Deterministic Quality Conformance Status which consequently determined the number of active variables that require close monitoring for achieving better overall conformance. The attribute chart, or rather P chart, was eventually constructed for the validation of the process based on the suspected active variables. This study reveals that the combinatorial based charts provided better process monitoring, once the active variables that serve as primary quality determinants are properly controlled. This eventually leads to the conformance of the entire process.Item On the Strength of Agreement between Initial and Final Academic performances in a Nigerian University System(ABACUS, Published by Mathematical Association of Nigeria, 2018) Banjoko, A. W.; Yahya, W. B; Abiodun, H. S.; Afolayan, R. B.; Garba, M.K.; Olorede, K. O.; Dauda, K.A.; Adeleke, M. O.This paper examines the strength of agreement between academic performances of students after their first and final years in the University. Academic performances of a total of 886 students that were admitted into various academic programs in the Faculty of Science, University of Ilorin, during the 2008/2009 academic session were followed-up to their year of graduation in 2012. Information on the grade point average (GPA) of students at the end of their first year in 2008, their final cumulative grade point average (CGPA) at the end of their studies in 2012 among others were collected. Results from this study generally showed a fair agreement between students’ initial and final academic performances in Nigeria University system (p < 0.001). It was also found that about 50% of students maintained the classes of degrees they had in their first year till graduation,about 40% of them improved on their performances while the performances of about 7% of them dropped from what they had at their firstyear.Further results showed that students’ performance is gender sensitive.Specifically, about 45% and 60% of female and male students maintained the classes of degrees they had during their first year in the University, about 50% and 30% of them improved on theirs while about 5% and 10% of them dropped from their initial academic performances at the end of their studies respectively. Finally, students in the Biological Sciences improved on their initial academic performances more than their counterparts in the Physical Sciences. Also, female students improved on their initial academic performances more than their male counterparts. This work will serve as useful counselling guide to prospective admission seekers into the Universities and all the stakeholders at enhancing students’ academic performances in the University system.Item REGULARIZATION TECHNIQUES IN MULTIPLE LINEAR REGRESSION IN THE PESENCE OF MULTICOLLINEARITY(Faculty of Physical Sciences, Federal University of Lafia, Nigeria., 2020) Oyegoke, O. A.; Oyeyemi, G. M.; Adeleke, M. O.; Kolawole, R. O.Multicollinearity has been a serious problem in regression analysis. Ordinary least square (OLS) regression may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity. Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), and Partial Least Squares (PLS) methods are well established methods that reduce the variability of the estimates by shrinking the coefficients and at the same time produce interpretable models by shrinking some coefficients. The performances of LASSO, Ridge Regression, PLS and OLS estimators were evaluated using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in the presence of multicolinearity using Monte Carlo Simulation. The simulations were done for different sample sizes: n (10, 50, 100, 150) and levels of multicollinearity: Mild (0.1 – 0.3), Low (0.4 – 0.6) and High (0.7 - 0.9). OLS had poor parameters estimate and produced wrong inferences, LASSO estimator is the best, while PLS is most efficient when the number of variables is greater than sample size.