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Although President Biden has been declared president-elect of the 2020 US presidential election, his road to victory was not a straightforward one, influenced by unexpected results from several swing states. This election was unique in many respects, but it was definitely not the first one to be impacted by results from crucial swing states. The main objective of this project is to analyze the impact of government decisions made from the Trump administration and other important events on election outcomes. First, the hierarchical clustering tool was used to group the 15 swing states based on the 2012, 2016, and 2020 election results, and the relationships between and within each cluster was further studied and attributed with events that may have factored into the cluster behavior. Next, a swing state index was devised to study the swing behavior of each cluster, in order to analyze how current situations may have factored into the election results of several states. The study of similar events and government decisions can be applied to future elections to better predict the outcome of important swing states.
This paper studies factors that influenced the voting behavior of 15 key swing states in the 2020 United States presidential election by linking statistical clustering methods with notable political events. In addition to key decisions made in the Trump administration, factors unique to this presidential election such as the global COVID-19 pandemic and the Black Lives Matter movement were investigated. In order to identify the most important swing states, a Swing State Index was derived using the 2012, 2016, and 2020 election outcomes. Next, hierarchical clustering was used to group the 15 swing states based on the Swing State Index, and the relationships between each cluster were attributed with events that may have factored into the cluster behavior. The most representative and significant swing states were identified to be Arizona, Georgia, Wisconsin, and Pennsylvania (based on the clustering history) as well as Michigan and Minnesota (based on the Swing State Index). After analyzing specific events that affected these six states’ voting behavior, the Black Lives Matter movement and concerns over health care were the most significant factors in President Trump’s defeat. Next, the state of Georgia was further studied to better understand the influence of COVID-19 and the economy on the state’s voting behavior. By adjusting the ratio of the COVID-19 values (infected cases and deaths) and economic value (unemployment rate), it was found that the economy was of greater importance than COVID-19 to Georgian voters. The study of similar events by connecting political science (e.g. government decision-making) and clustering methods can be applied to future elections to better predict the outcome of important swing states and, thus, the overall election results.
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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