<![CDATA[This research aims to identify significant factors influencing the poverty level in Tuban Regency. Multiple linear regression analysis using the Ordinary Least Squares (OLS) method was applied using the Google Colab platform and the Scikit-learn library to examine the relationship between the Human Development Index (HDI), Open Unemployment Rate (OUR), and Gini ratio on the poverty level. Before the regression analysis was conducted, the data were tested for normality using the Shapiro-Wilk test to ensure that the classical regression assumptions were satisfied. Then, a correlation matrix is presented to provide an initial overview of the relationship between variables. The analysis results show that the HDI is the dominant factor that significantly contributes to the decrease in the poverty level in Tuban Regency. Meanwhile, the unemployment rate and the gini ratio do not show a significant influence in this regression model. Visualization of actual data and model data shows a fairly good fit, indicating the model's accuracy in explaining the variability of the number of poor residents.]]>
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