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Using Simple Linear Regression in Excel to Predict College Innovation Output

In our previous example we explored open data from the Opportunity Insights team’s paper about how higher education institutions provide pathways in intergenerational upward social mobility. We used this data and open Pell Grant data from the CollegeScoreboard to determine if using the percentage of students who received Pell Grants at an academic institution was a good metric to evaluate Johns Hopkins University’s role in US intergenerational upward mobility and what additional metrics Johns Hopkins University should consider monitoring to determine their progress in educating a diverse group of students and “cultivate their capacity for lifelong learning, to foster independent and original research, and to bring the benefits of discovery to the world.”

While we know that student socioeconomic diversity may contribute to better learning outcomes and therefore better research discoveries and job placements, what other metrics can Johns Hopkins University measure to determine if their institution is succeeding compared to peer institutions? We’ll use another data source from the Opportunity Insights team that documents college “innovation rates” (cataloging the number of patents and citations from each academic institution as long as there are greater than 100 patents) in addition to our previously used data sets to look at other student and financial metrics that may help us determine what factors contribute to an academic institution’s academic success and innovativeness and how Johns Hopkins University can foster that within a growing diverse student population for future success.

How can academic institutions--and Johns Hopkins in particular--become more innovative?

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Using Baltimore City Open Data to show how to perform a simple linear regression analysis in Excel

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