The Case of the Forgotten Something or Other
The following is a Prezi presentation using storytelling.
As data detectives, we often use our manifying glass to find the story. In fact, just when we think that there isn't one, we stumble upon another clue that often leads us on a tasty trail.
Click the center to join on the fun.
Go for the Gold: Using Economics to Make Predictions
Like many Americans this summer, you probably tuned into the 2012 Olympic Games to watch your favorite sports and favorite athletes go for the gold. While you were rooting for Michael Phelps and Ryan Lochte, did you try to guess how many medals the United States would come home with this year? Whether they would come out on top against close competitor, China? Guess no more; an economics professor has developed a model to predict Olympic medal wins with 94% accuracy.
Dan Johnson is an economics professor at Colorado College. He has been predicting medal winners since the 2000 Summer Games in Sydney using his model. It started off as an experiment to see if economics really could be used to predict medals. Johnson didn't actually think it would work, but it did. The incredible thing about his predictions though is that he doesn't use data from individual athletes or their events. He uses strictly economic measures like the country's per-capita income, population, political structure, climate, and the home-field advantage if they are hosting the games.
This year he correctly guessed that the United States would win the most medals with 99 total medals, while the actual count was 104. He also predicted that Russia would come in second and China third; in reality the two were switched. However, his prediction of Great Britain coming in fourth and Germany fifth proved to be right on the money as well as his prediction of Russia winning 82 medals.
So if models can be used to predict something that has so many variables and emotions like a sporting event, what else can they be used to predict? This may come as a surprise, but economic models are used all the time here on the Binghamton University campus. Enrollment Management, for example, uses models to make predictions about incoming students. One of their main statistics is yield rate (percent of accepted students who actually enroll). Enrollment Management uses yield rates and other variables to determine how many students to accept each year. Residential Life uses similar models to determine the number and type (male/female) of housing vacancies there will be on the first day of the term. Like Professor Dan Johnson does with his model, these models are constantly tweaked using new data to improve their accuracy.
To leave you with some food for thought... how does your department use statistics and models? They could be used to predict anything from how many office supplies to order to how many students to accept each year. So keep track of your statistics and use those numbers to your advantage! In the long run it could save you time, money, stress, and help you make well-informed decisions.
Click here to read more about Dan Johnson and his Olympic medal prediction model.
