Statistical Modeling Yields Accurate Election Forecasts

Statistical modeling techniques that businesses use to find customers helped quantitative analysts predict the results of this month's U.S. elections with stunning accuracy.

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Statistical modeling techniques that retailers and manufacturers use to find and target customers helped some prognosticators predict the outcome of this month's U.S. elections with stunning accuracy.

Because of his connection to The New York Times, blogger Nate Silver may be the best-known quantitative analyst to accurately predict the election results, but many others also used statistical models and got similar results. The spot-on forecasts have focused unprecedented attention on quants, as quantitative analysts are known, and their ability to predict future events and trends.

As far back as June, Drew Linzer, an assistant professor of political science at Emory University, predicted in his blog, Votamatic, that Barack Obama would win re-election with at least 52% of the popular vote and 332 Electoral College votes. In the end, Obama took 51% of the popular vote and 332 electoral votes.

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