Graduation Rates is a rising concern in the US. With limited financial and human resources, regional variation , and a myriad of contributing social, economic, and individual factors, increasing graduation rates proves challenging. The Graduation Rate Estimator, developed in Tableau, identifies factors and creates a model to predict future graduation rates. By combining several open data sources including YRBSS, NSCH, and US Census, the dashboard captures a rich dataset of social, economic, and individual factors in a visually compelling and meaningful way.

Multiple regression in SPSS was used to understand whether graduation rates can be predicted based on school district, county, and state level indicators. More specifically the goal was to understand how much of the variation in graduation rates can be explained by these indicators "as a whole", but also the "relative contribution" of each independent variable in explaining the variance. The contribution of each variable was used to develop a dashboard that predicted graduation rates based on user inputs to empower administrators with information to better focus resources, to minimize the risk, and develop interventions where needed.

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