Micronumerosity in classical linear regression

  • G. M. Oyeyemi
  • A. Bolakale
  • A. I. Folorunsho
  • M. K. Garba
Keywords: Micronumerosity, Multicollinearity, Linear Regression, Principal Component Analysis, Factor Analysis

Abstract

This 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.

Key Words: Micronumerosity, Multicollinearity, Linear Regression, Principal Component Analysis, Factor Analysis

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eISSN: 1118-1931
print ISSN: 1118-1931