Browsing by Author "Ibraheem, B. A."
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Item Analyzing Factorial Experiment Involving Qualitative and Quantitative Factors.(African Network of Scientific and Technology Institutions, Kenya., 2009) Oyeyemi, G. M.; Ibraheem, B. A.; Obafemi, O. S.; Ige, O. S.In factorial experiment involving qualitative and quantitative factors, ascertaining whether there is interaction between the two factors should be the first objective in the analysis. In this paper, we explore the different possibilities that may arise when dealing with qualitative and quantitative factors in a factorial experiment. The response curves for the quantitative factor are different across the levels of the qualitative factor when there is interaction between the factors, the curves are parallel but similar with distance apart when there is no interaction but both factors are significant, while curves converged to a curve (become a single curve) when the quantitative factor is not significant.Item Comparison of some Robust Estimators in Multiple Regression in the Presence of Outliers(Akamai University, U.S.A, 2021) Oyeyemi, G. M.; Aji, D. A.; Ibraheem, B. A.; Kareem, A. O.Outlier results are one of the problems of Ordinary Least Squares (OLS) in regression analysis. Some estimators have been suggested as alternatives to the Ordinary Least Squares (OLS) estimator to improve the accuracy of the parameter estimates in the linear regression model in the presence of outliers. In this study, six robust estimators of handling the problem of outliers: Robust-M, Robust-MM; Robust-S; Least Trimmed Squares (LTS); Least Median Squares (LMS); and Least Absolute Deviation (LAD) were compared with OLS using Variance criterion. The multiple linear regression model considered, had 4 predictor variables (p = 5) and one dependent variable and there were four levels each of percentage of outliers (10%, 20%, 30%, 40%), variance of outliers (σ_outlier^2=1,50,100,200) and sample sizes (n = 20, 50, 100, 200) were considered through Monte Carlo experiments. The experiment was carried out 1000 times. The results showed that when the variance of outlier is 1, that is, the outliers and variables have standard normal distribution, OLS had the least variance at all sample sizes. But as the variance increases and at all sample sizes, the robust estimators outperformed the OLS. The robust MM had least variance more consistently as the sample size increases at all variance level of the outlier and also as the sample size increases. Therefore, the Robust MM Estimator performed more consistently than the other robust estimators considered.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 Multivariate Regression in Complex Survey Design(International Center for Advance Studies, India, 2009) Oyeyemi, G. M.; Adewara, A. A.; Ige, O. S.; Ibraheem, B. A.In multivariate regression analysis of complex survey design, adjustment for both stratification and clustering variable depends on whether the variables are exogenous or endogenous in the model. Adjustments are found to be necessary if the variables are exogenous in the models, especially adjustment for clustering is found to be more important than that of stratificationItem 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 Spatial Analysis of Distribution of Inmates in Nigeria Prisons(Faculty of Physical Sciences, University of Ilorin, Nigeria, 2022) Oyeyemi, G. M.; Kareem, A. O.; Alemika, E. E. O.; Ibraheem, B. A.; Oladuti, O. M.The number of prison facilities available in Nigeria differs from state to state as well as across the regions. Hence the distribution of the inmates across these correctional facilities differs. This study investigates the distribution of the inmates across Nigeria correctional facilities (prisons) and the likely factors that may influence the distribution considering the spatial effect of the distribution. The considered factors are; year, month, state, region, gender and state population. Ordinary Least Square (OLS) was used to obtain the multiple regression model and Moran’s I test of spatial autocorrelation showed that there is presence of spatial autocorrelation in the distribution of inmates across Nigeria prisons. The Spatial Autocorrelation Regression Model (SARM) was therefore adopted to fit the regression model. While OLS approach indicated that all factors are significant except state, the SARM approach found both state and region not to be significant among the factors considered in the model. The SARM did not only give good compact spatial distribution of the inmates across Nigeria prisons it also gave a better predicted (forecast) values of inmates’ distribution than the OLS approach.