<![CDATA[Purpose: This study aims to examine the multivariate relationship between student intake factors and first-year academic performance in mathematics-based programs. Method: A quantitative explanatory design was applied using a multivariate regression approach. The study involved 257 students from Mathematics, Statistics, and Actuarial Science programs at a public university in Indonesia, representing cohorts from the 2017/2018 to 2023/2024 academic years. First-year academic performance was measured using final grades from eight interrelated foundational courses. Predictor variables included prior educational curriculum, average high school mathematics scores, university admission pathways, motivation for program selection, and selected demographic characteristics. Data analysis comprised model specification, ordinary least squares parameter estimation, multicollinearity testing, simultaneous and partial significance testing, and evaluation of multivariate regression assumptions to ensure the robustness of the model. Findings: The results indicate that student intake factors collectively have a significant multivariate effect on first-year academic performance. Prior mathematics achievement, educational curriculum background, admission pathways, and motivation for choosing the study program emerged as key predictors, with varying magnitudes and directions of influence across courses. These findings reflect the multidimensional and interrelated nature of academic performance in mathematics-based programs. Significance: This study reinforces academic preparedness theory and provides empirical evidence to support data-driven student selection policies, early academic intervention strategies, and curriculum alignment in higher education institutions.]]>
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