This thesis explores the importance of prerequisite courses on future performance, exclusively using the available transcript data at the University of Manitoba. Our analysis is based on data from Fall 2009 to Winter 2018, including information about students who took STAT 3400 (Introduction to Probability II), along with all grades for all student attempts in this course and the relevant prerequisite information. Specifically, we are interested in the relevance of specific prerequisites on the grade outcomes in STAT 3400, a core course in the Statistics program.

Beginning with an exploratory analysis of the provided data to determine any challenges presented in this categorical data. We aim to take a technically sound approach by exploring different transformations of the data, groupings, and models. We explore existing structures of categorical models and methodologies to gain better insights into our data. In doing so, we demonstrate how our sequential logistic regression model is parallel to the multinomial logistic regression model, but offers different interpretations and naturally follows the order of these categories.