Browsing by Author "Adeleke, B. L."
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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 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 Examining the impact of Sample and Effect Sizes on the Power of One-way Analysis of Variance F-test.(Research India Publications, 2012) Oyeyemi, G. M.; Adeleke, B. L.The power of a test is a function of three factors; sample size, the size of detectable effects and the level of significance. The paper focused on determining the non-centrality parameter in one-way analysis of variance (ANOVA) test which by implication utilizes the completely randomized design. To examine the contributing effect of sample size and size of detectable effects, data were simulated for varying levels of treatments, replicates and effect sizes for several experiments of the completely randomized design type. The results showed that power of the F test increased monotonically with size of non-centrality parameter λ, where non-centrality parameter is a function effect size. An increase in sample size also brings proportionate increases in power irrespective of the number of treatments under consideration.Item Identification of Significant Treatment Effects in Unreplicated Factorial Experiments.(Published by Biometrics Association of Nigeria., 2006) Ibraheem, B. A.; Adeleke, B. L.; Oyeyemi, G. M.Unreplicated factorial designs are used in industrial and other real-life experiments due to costs consideration or some other reasons. Normal plot has been an important technique of identifying significant treatment effects. However, two other methods of identifying significant effects namely, Lenth and Step-Down Lenth methods are examined in this paper. The three methods are compared in terms of their performance in identifying significant effects by simulating unreplicated 24 factorial designs at two different settings. In evaluating the three methods, we observed that Normal plot has the overall best performance, but the three methods are similar when the magnitude of each of the effects is high.Item J2 Optimality and Multi-level Minimum Aberration Criteria in Fractional Factorial Design(International Institute for Science, Technology & Education., 2012) Salau, I. S.; Adeleke, B. L.; Oyeyemi, G. M.The desirable properties of fractional factorial design: Balance and orthogonal; was examined for near balance and near orthogonal using the balance coefficient and J2 optimality criteria respectively. Efficient orthogonal arrays with three factors having two, three and four levels were constructed with balance and orthogonal property for lowest common multiples of runs. The two forms of balance coefficient were used for classifying the designs into two and multi level minimum aberration criteria were used to determine designs with lesser aberration. It was observed that designs constructed using the maximum form of balance coefficient has the lesser aberration in both the generalized minimum aberration and minimum moment aberration criteria. The J2 – optimality criterion reveals that the higher the run of a design, the lesser it’s optimality value.Item A PROPOSED METHOD OF IDENTIFYING SIGNIFICANT EFFECTS IN UNREPLICATED FACTORIAL EXPERIMENTS(Anale Seria Informatica, 2019) Ibraheem, B. A.; Adeleke, B. L.; Oyeyemi, G. M.In many areas of research/ production, a lot of factors are combined to obtain a desired product. To be able to analyze which factors (or combinations of factors and at what level) are significant, the experiment has to be replicated. For economic or practical reasons, it may not be feasible to perform the experiment more than once therefore unreplicated factorial designs are often employed. This is especially true in the field of Medicine, Pharmacy and Industrial production units. The traditional method of analysis of variance (ANOVA) cannot be employed in unreplicated factorial designs, therefore many methods have been proposed in literature. In this paper, a new method of analyzing unreplicated factorial designs is proposed and was compared with some of the existing methods. The four existing methods considered were: Lenth, Berk and Picard, Juan and Pena, and Dong. The comparison was performed using Monte Carlo simulation method. The criteria used in evaluating the performances of the methods are Power and Individual Error Rate (IER). Using these criteria of evaluation, the results showed that on overall performance, Dong method is the best among the four existing methods considered and was closely followed by Berk and Picard, Lenth, then Juan and Pena methods in that order. It was also found that not only is the proposed method simpler to compute, it competed favourably with Dong and even performed better than all the others when IER is used for assessment.Item Review of Classical Methods in Supersaturated Designs (SSD) for Factor Screening(The International Institute for Science, Technology and Education (IISTE), 2015) Salau, I. S.; Adeleke, B. L.; Oyeyemi, G. M.Supersaturated designs are fractional factorial designs that have too few runs to allow the estimation of the main effects of all the factors in the experiment. There has been a great deal of interest in the development of these designs for factor screening in recent years. A review of supersaturated design is presented, including criteria for design selection, with reference to the popular E(s2) criterion and classical methods for constructing supersaturated designs. Classical methods have been suggested for the analysis of data from supersaturated designs and these are critically reviewed and illustrated.Item Sequential Analysis of Mean for Test Equality of Several Means(Virginia Tech., USA, 2011) Oyeyemi, G. M.; Adeleke, B. L.The of Analysis of Means (ANOM) and Sequential Analysis of Means (SANOM) as graphical statistical techniques for comparing group of treatment means were examined in this paper. Results arising from the ANOM and SANOM were compared with the conventional (parametric) approach, Analysis of Variance (ANOVA) and its Post Hoc analysis. Apart from its simplicity for non-statistician, ANOM and SANOM were found to be more efficient, precise and unambiguous especially in its post hoc analysis (SANOM) for producing non-overlapping homogeneous subgroups in the separation of means.