Micronumerocity in Classical Linear Regression

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dc.contributor.authorOyeyemi, Gafar Matanmi
dc.contributor.authorBolakale, AbdulHamid
dc.contributor.authorFolohunsho, A. I.
dc.contributor.authorGarba, Mohammed
dc.date.accessioned2021-10-12T11:59:10Z
dc.date.available2021-10-12T11:59:10Z
dc.date.issued2015-04-12
dc.description.abstractThis study studied the problem of micronumerosity in CLR in other to prescribe appropriate remedy to the problem if encountered at any CLR analysis. The study is aimed at determining an optimum sample size n*, such that when the number of observations of variables in CLR is greater than (i.e. n > n*) then micronumerosity is not a problem. It also suggests means of correcting micronumerosity in CLR. The optimum minimum sample size (n) for a given number of independent variables (p) and level of correlation between the dependent and independent variable(s) were determined. Also, Factor Analysis served as the best method of overcoming problem of micronumerosity.en_US
dc.identifier.issn1118-1931
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/6599
dc.publisherScientia Africana. Published by College of Natural and Applied Sciences, University of Port Harcourt, Nigeria.en_US
dc.relation.ispartofseries14;1
dc.subjectMicronumerosityen_US
dc.subjectMulticollinearityen_US
dc.subjectLinear Regressionen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectFactor Analysisen_US
dc.titleMicronumerocity in Classical Linear Regressionen_US
dc.typeArticleen_US

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