Oyeyemi, G. M.Bolakale, AFolorunsho, A. I.Garba, M. K.2023-07-272023-07-272015Scientia Africana1118 - 1931https://uilspace.unilorin.edu.ng/handle/20.500.12484/11619This study studied the problem of micronumerosity in Classical Linear Regression (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 micronemerosity is not a problem. It also suggests means of correcting micronumerosity in CLR. The 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.This study studied the problem of micronumerosity in Classical Linear Regression (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 micronemerosity is not a problem. It also suggests means of correcting micronumerosity in CLR. The 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.enMicronumerosity, Multicollinearity, Linear regression, Principal component analysis, Factor analysisMicronumerosity, Multicollinearity, Linear regression, Principal component analysis, Factor analysisArticle