This study compared application data for an undergraduate nursing program at a public health science center university in the southeast. The study covered five years of application cohorts from 2009 to 2014 from admission through graduation. The application data analyzed two dependent variables to predict students likely to achieve success in the nursing program. These two variables to measures success were: the ability of the admitted student to successfully achieve graduation requirements; and those graduates that were able to pass the national nursing certification exam on the first attempt. The application data was assessed to determine if a relationship existed between the data used to select students for admission and the success outcomes from an undergraduate nursing program. The application data was analyzed using a logistic regression and decision tree model to explore the relationship between the variables. The scores provided by the faculty members’ overall assessment of the entire application file was significant in three of the four logistic regression models and race was significant in the national certification logistic regression model. A similar finding resulted with scores provided by the faculty members’ overall assessment of the entire application file placed as the first node in three of the four decision tree models, and race placed as the first node in the national certification exam decision tree model. The study found that the data provided by faculty members in the admission process yielded results with the highest predictability related to student success in a nursing program.