Segmenting Clinton and Obama Voters

Abstract

The purpose of this case is to introduce data visualization, advanced regression techniques, and supervised learning. Students are asked to visualize data geographically and in scatterplots. They will use stepwise regression and regression trees to select a predictive model for forecasting data in a holdout sample. In a forecasting competition, they will submit their models to be tested for accuracy. Supervised learning techniques—such as training, validation, and testing—are introduced. Regression trees serve as both predictive and graphical tools for communicating insights from data analysis to a decision maker. Excerpt UVA-QA-0807 Rev. Sept. 21, 2017 SEGMENTING CLINTON AND OBAMA VOTERS It was February 19, 2008. One week earlier, Barack Obama had taken the lead in the delegate count during the Democratic Party's presidential primaries, the winner of which would face the Republican Party's nominee in the general election to become the next president of the United States (POTUS). On that day in February, Hillary Clinton, Obama's primary opponent, began running ads in Ohio aimed at middle-class, blue-collar voters. One ad, “Night Shift,” closed showing Clinton at her desk: “She understands. She's worked the night shift, too.” But had Clinton ever worked the night shift? Her spokesperson said it was a “rhetorical reference” to working late nights as a lawyer, First Lady, and senator. Clinton was not alone in her awkward appeals to voters in key demographics. Months earlier at a campaign stop in Iowa, Obama noted that while produce prices had risen in grocery stores, farmers had not benefited from increases in crop prices: “Anybody gone into Whole Foods lately and see what they charge for arugula? I mean, they're charging a lot of money for this stuff.” At the time, there wasn't a single Whole Foods in the state of Iowa. . . .