This analysis and presentation used publicly available data that can be found at https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success. The included Excel dataset replaced the numerical labels for machine learning with categorical labels. This preliminary work shows that financial information, such as if a student is up to date on tuition and if a student is a scholarship holder, is moderately related to whether a student will drop out or not. The presentation uses Power BI visuals, and the analysis consisted of chi-squared hypothesis testing.