Browsing by Author "Adeyemi, R. A."

Now showing 1 - 3 of 3
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    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 endogenous
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    Complex Survey Data Analysis: A Comparison of SAS, SPSS and STATA
    (Asian Network for Scientific Information, 2010) Oyeyemi, G. M.; Adewara, A. A.; Adeyemi, R. A.
    We compared three statistical packages (SAS, SPSS and STATA) in analzing complex survey data in the context of multiple regression analysis using concrete examples from two national healthcare database (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 over estimate the variances of the sample statistics. Adjustment for stratification (incorporating stratification) is very important in complex survey analysis, especially if the stratification variable is endogenous
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    Time Series Prediction Based on Genetic Algorithm with Application in Finance
    (PAN African Book Company, 2008) Adeyemi, R. A.; Oyeyemi, G. M.
    Real world problems are described by non-linear and chaotic processes, which makes them hard to model and predict. The aim of this paper is to determine the structure and weights of a time series model using genetic algorithm (GA). The paper first describes the traditional procedure of estimating time series models, which are commonly used in financial forecasting. These traditional estimation methods may not be adequate enough to capture stochastic nature of the financial due to its complexity. This article gives a brief background of Genetic algorithm method and its estimation procedure. This approach was later applied to model the Naira exchange rates against other currencies and it yielded a mean square error of 0.0058, 0.00799, 0.03711, 1.212 and 0.1108 for U.S dollars, British Pound, Jopanese Yen, CFA franc and Swiss franc respectively