This project investigates different strategies Trump can utilize in reopening states during the COVID-19 pandemic, as well as the validation of a prediction model and the actual 2020 result of the 2020 United States Presidential Election. During the pandemic, the President and state governors face the challenge of deciding when to reopen the states and whether they should all be opened all at once or based on their individual situations. Based on several calculated variables, such as past win margins of swing states, infected cases, deaths, and unemployment increases for 15 different swing states from past elections, the authors draw conclusions on which states President Trump should be put into consideration to “liberate” or “reopen” to not only safely reopen, but to maximize his chances of winning the 2020 election. With the calculated and collected variables, a statistical model is created to aid in decision making. Although the safest option is to stay closed, many state economies and the overall national economy suffer due to the closure. Trump’s pro-economy campaign must wisely select which states to liberate based not only on unemployment rates, but chances of winning that state in the upcoming 2020 election. This project plays special attention to Michigan, Minnesota, and Virginia, which were called out in President Trump’s tweet on April 17, 2020. Ultimately, this project concludes that Minnesota is a safe state to liberate, Michigan is too risky, and Virginia can be liberated, but the authors advise against it. Additionally, a prediction model is created considering the impacts of COVID-19 and unemployment rates. The prediction model consists of the predicted election result, which is derived from the z-scores of the number of infected cases, deaths, and unemployment increase rates for each of 15 swing states and the 2012-2016 election result average. The predicted election result is then subtracted in response to the media’s report about how Donald Trump is expected to lose 3-5% of his votes from the 2016 election. The model is used to compare the level of accuracy between the predicted 2020 election result and its subtracted values against the 2020 actual election result. The paired t-test and regression test are used to test the significance between the 2020 actual result and 2020 predicted result as well as the 2016 actual result and 2012 actual result to see how the 2020 predicted result compares with the 2016 election result and 2012 election result in predicting the 2020 actual result. A 1-proportion hypothesis test is also used to compare the accuracy of the 2020 predicted result with the 2020 actual result. All calculations and analysis are done on the JMP 16 statistical analysis software. This project demonstrates a practical and statistical modeling framework considering social and political science factors.
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