Browsing by Author "OYEYEMI, G. M."
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Item Complex Survey Data Analysis: A Comparison of SAS, SPSS and STATA(Asian Network for Scientific Information, Pakistan., 2010) OYEYEMI, G. M.; Adewara, A. A.; Adeyemi, R. A.We compared three statistical packages (SAS, SPSS and STATA) in analyzing complex survey data in the context of multiple regression analysis using concrete examples from two national healthcare databases (MEPS and NDHS). The three packages are found to be efficient and flexible in analyzing complex survey data, but SAS in some cases seems to overestimate the variances of the sample statistics. Adjustment for stratification (incorporating stratification) is very important in complex survey analysis, especially if the stratification variable is endogenousItem Employment Generation and Earnings in the Informal Transport Sector in Nigeria.(Canadian Academy of Oriental and Occidental Culture, 2011) Arosanyin, G. T.; Olowosulu, A. T.; OYEYEMI, G. M.Unemployment and poverty are two of the challenges facing the Nigerian economy. Most urban semi and unskilled labour have found solace in the informal sector particularly informal transport. This paper examines employment generation and determinants of earnings in the informal transport sector in Nigeria using a case study. An adapted Mincerian equation and logistic models were used as tools of analysis. It was found that the informal sector is a source of employment for 21.7 per cent of jobless people; and 72.3 per cent of those who switched jobs from an informal activity to transport business. Household size, experience and operating hours were found to be significant determinants of earnings. The probability that a motorcyclist would earn at least the informal average in the Okada business when the operator has a driver license, owns the motorcycle, works on full time basis and also a member of the okada union is 0.8018, which is higher than that of an operator with the reverse attributes at 0.2849. The probability of earning at least the industry average by an educated operator was found to be higher than less educated operators. Employment and earnings can be improved upon in this sector if the government regulates its operations and segregate traffic.Item On The Estimation of Power and Sample Size in Test of Independence(Asian Network for Scientific Information, Pakistan, 2010) OYEYEMI, G. M.; Adewara, A. A.; Adebola, F. B.; Salau, I. S.In this study, power and sample size estimations in the context of test of independent between categorical variables were examined. The required sample size in an experiment is a function of the alternative hypothesis, the size of type I error and the variability of the population. Power of a test is the probability of rejecting the a false null hypothesis and it depends on the effect size, type I error and sample size. A priori power analysis is determination of minimum sample size to obtain a required power while post-hoc power analysis is calculating power of a test. A test with small effect size requires large sample size to achieve a power of 80% or more while effect size of medium or large size needs small sample size to achieve that. Test with small degrees of freedom will attain higher power than the same test with larger degrees of freedom.Item Some Robust Methods of Estimation in Factor Analysis in the presence of outliers(International Center for Advance Studies, India, 2010) OYEYEMI, G. M.; Ipinyomi, R. A.A robust method of estimating covariance matrix in multivariate data set is proposed. The goal is to compare the proposed method with the widely used robust methods (Minimum Volume Ellipsoid and Minimum Covariance Determinant) and the classical method (MLE) in the area of Factor Analysis. MVE- MCD- and Proposed- factor analyses use robust covariance matrix in estimating the factor loadings while the classical factor analysis estimates the factor loadings using the MLE of the covariance matrix. While classical factor analysis is found to be more reliable when there are few or no outliers in the data, robust factor analysis will be preferred in the presence of multiple outliers. The Proposed Robust Factor analysis is found to compete favourably well with existing robust methods.Item Stand-in Procedure to Multivariate Behrens-Fisher Problem(Akamai University, U.S.A, 2018) OYEYEMI, G. M.; Adebayo, P. O.This work considers the problem of comparing two multivariate normal mean vectors under the heteroscedasticity of dispersion matrices. We develop a new procedure using approximate degree of freedom method by Satterthwaite [23] and broaden it to Multivariate Behrens-Fisher. The New procedure is compared with existing ones via R package simulation and Data used by James [8] and Yao [31]. We ascertain that, new procedure is better in terms of power of the test and type I error rate than all existing procedure mull over when the sample sizes are not equal, but the proposed procedure perform the same with the selected procedure when sample sizes are equal.Item Treatment of Non-normal Responses for Designed Experiments(Nigeria Statistical Association, 2004) OYEYEMI, G. M.Many experimental designs, most especially industrial designs produce non-normal response variables. The Least Squares method of modeling may therefore not produce efficient estimates. Models are built using B-technique and Box-Cos methods of data transformation or by using GLM to overcome the non-normality nature of the data. In this paper, these techniques are compared using histograms of the estimated mean responses and by examining the length of the confidence interval about the mean responses. It is observed that B-technique is the best method of data transformation but GLM provides an excellent alternative if the experimental design points are nor replicated.