Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration and timeline of the critical events, which are also a binary and dichotomous measure. This paper introduces logistic and Cox regression models by illustrating examples, implementing step-by-step SPSS procedures, and further comparing the similarities and differences of the model characteristics. Logistic regression analysis was conducted to investigate the effects of the explanatory variables such as pre-admission variables, college cumulative GPAs, and curriculum tracks on student licensure examination. Moreover, logistic regression analysis was employed to quantify the effect (odds or odds ratio) of specific explanatory variables on the binary outcome holding other variables constant. With regards to Cox regression analysis, the outcome variable of interest was the timing of experiencing academic difficulty--dismissal, withdrawal, and leave of absence. The Cox regression model was used to detect when students were most likely to experience academic difficulty beyond their matriculation. The model also allowed the investigators to measure the effect (relative hazard or hazard ratio) of specific risk factors on the academic difficulty after adjusting for other factors. Identifying the occurrence of critical events along with the explanatory variables, college administrators and faculty could implement intervention strategies to ensure student success. (Contains 1 figure and 6 tables.)