PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM

作者： | Abubakari S.Gwelo |

刊名： | Oradea Journal of Business and Economics (OJBE), 2019, Vol.4 (1) |

来源数据库： | University of Oradea, Faculty of Economic Sciences |

关键词： | Principal components; Multicollinearity; Variance inflation factor.; |

原始语种摘要： | The impact of ignoring collinearity among predictors is well documented in a statistical literature. An attempt has been made in this study to document application of Principal components as remedial solution to this problem. Using a sample of six hundred participants, linear regression model was fitted and collinearity between predictors was detected using Variance Inflation Factor (VIF). After confirming the existence of high relationship between independent variables, the principal components was utilized to find the possible linear combination of variables that can produce large variance without much loss of information. Thus, the set of correlated variables were reduced into new minimum number of variables which are independent on each other but contained linear combination of the... |