Education Disparity in Chicago Public High Schools: A Statistical Analysis

Abstract

The purpose of this paper is to delve into the Chicago public high school system to extract information on demographics and academics. The methods used are common in machine learning and discrete mathematics and seek to bring helpful visualizations and interpretations of education disparity. Not only is the goal to analyze the current state but also how these demographic and academic factors have changed over time for certain school groups. This can accomplish this using graphical models and create visualizations to show key factors and how the change over time. In addition, one can see how the COVID-19 pandemic has affected schools in this system. Lastly, the correlations between these factors will be presented along with discussion.