Mengatasi Multikoliniearitas Dalam Regresi Linier Berganda Menggunakan Principal Component Analysis - Chairunnisa, Niken Harel; Darnah, Darnah; Syaripuddin, Syaripuddin (Program Studi Statistika FMIPA Universitas Mulawarman, 2025)
Multiple linear regression analysis has assumptions that must be met, one of which is multicollinearity. Multicollinearity occurs when the independent variables correlate with each other, resulting in the regression coefficient produced by multiple linear regression analysis being very weak or unable to provide analysis results that represent the nature or influence of the independent variable concerned. The detection of multicollinearity can be known through the VIF value. In this study, human development index data on Kalimantan Island in 2019 detected multicollinearity because some independent variables have a VIF value of more than 10 so that the method used to overcome multicollinearity in this study is Principal Component Analysis (PCA). Based on the results of research using the Principal Component Regression method, There are five independent variables that influence the IPM that is Percentage of Poor Population, Number of Health Workers, Number of Workforce, Number of High Schools, and Number of High School Teachers.