Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Matthew V Chin, Senior Timothy A Smith, Faculty Albert J Boquet, Faculty
Lead Presenter's Name
Matthew V Chin
Faculty Mentor Name
Timothy A Smith
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Abstract
In an application of the mathematical theory of statistics, predictive regression modeling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple "predictor" input variables. In this study, the social distancing was measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done with county level data obtained from the State of Florida during the COVID-19 pandemic. The predicting factors found that were most deterministic was the county population density along with median income.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
A Statistical Learning Regression Model utilized to determine predictive factors of social distancing during COVID-19 pandemic
In an application of the mathematical theory of statistics, predictive regression modeling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple "predictor" input variables. In this study, the social distancing was measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done with county level data obtained from the State of Florida during the COVID-19 pandemic. The predicting factors found that were most deterministic was the county population density along with median income.