<![CDATA[This study examines the statistical determinants of graduate income using Maximum Likelihood Estimation implemented through the Iteratively Reweighted Least Squares algorithm. A generalized linear model with a logit link was estimated to assess how technical competence, English proficiency, and tuition-financing type influence the probability of earning above the regional minimum wage. The IRLS procedure enabled iterative refinement of coefficient estimates and standard errors, with convergence achieved as successive updates stabilized, indicating a well-behaved likelihood surface. Results show that technical competence and English proficiency significantly increase the log-odds of higher income, highlighting the importance of human-capital attributes in early-career outcomes. Tuition-financing type appears as the strongest predictor, with scholarship-funded graduates demonstrating substantially higher income likelihood than their self-funded peers, suggesting structural advantages associated with financial support. Although the model demonstrates meaningful associations, its moderate discriminative capacity underscores that pre-graduation characteristics alone cannot fully explain income variation. By explicitly incorporating MLE with IRLS, this study provides a robust statistical framework for analyzing employability patterns and contributes methodological clarity to graduate income research. The findings encourage future work integrating richer labor-market indicators and continuous income measures to enhance predictive accuracy.]]>
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