This study proposes a Cox regression model with a Weibull baseline hazard, representing a key statistical innovation, hereafter referred to as the Cox Weibull regression model. Parameter estimation was conducted separately, the regression parameters were estimated using the partial likelihood method, while the baseline hazard parameters were estimated using Maximum Likelihood Estimation (MLE). This study aims to identify factors influencing graduation rates and to examine variations in these rates among students in the Statistics Study Program at Universitas Mulawarman based on significant factors. The study used academic records of 77 students from the Statistics Study Program (cohorts 2016–2019), obtained from the Academic and Student Affairs Bureau, Faculty of Mathematics and Natural Sciences, Universitas Mulawarman. The best model selected using the backward elimination method, included three covariates and was considered adequate, as all covariates were statistically significant and the model yielded the lowest AIC value (462.34). The results indicate that cumulative grade point average (GPA), scholarship status, and university admission pathway significantly influence student’s graduation rates. Higher GPA was associated with a shorter time to graduation, scholarship recipients graduated faster than non-recipients, and students admitted through the SNMPTN pathway graduated faster than those admitted through other pathways.
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