<![CDATA[Multicollinearity is one of the problems in linear regression that can lead to unstable parameter estimates. This study aims to address multicollinearity issues in multiple linear regression models applied to poverty data in Central Java Province using the Jackknife Ridge Regression method. The data used are secondary data from the Central Java Provincial Statistics Agency for 2022-2023, with the poverty rate as the dependent variable and eight independent variables representing poverty-related factors. This research was conducted using a literature review method and data analysis with R software. The results show that the Jackknife Ridge Regression method successfully mitigates multicollinearity, producing an accurate model. The final model indicates that average years of schooling, life expectancy, labor force participation rate, human development indeks, and regional gross domestic product have a negative effect on the poverty rate. These findings highlight the importance of improving education quality, healthcare, human development, and access to basic infrastructure as key strategies for poverty alleviation in Central Java Province.]]>
Copyrights © 2025
