{"id":23066,"date":"2016-10-10T21:43:17","date_gmt":"2016-10-11T02:43:17","guid":{"rendered":"http:\/\/scienceblogs.com\/gregladen\/?p=23066"},"modified":"2016-10-10T21:43:17","modified_gmt":"2016-10-11T02:43:17","slug":"who-will-win-the-presidential-race","status":"publish","type":"post","link":"https:\/\/gregladen.com\/blog\/2016\/10\/10\/who-will-win-the-presidential-race\/","title":{"rendered":"Who will win the presidential race?"},"content":{"rendered":"

I’ve made my first stab at a prediction for the electoral college outcome for the US Presidential race, 2016. I use a roughly similar methodology as I did to accurately predict most of the Democratic primaries. However, since primaries are different from a general, the methodology had to be adapted.<\/p>\n

For the primaries, I eventually used this methodology. I used results form prior primaries to predict voter behavior by ethnicity, in order to predict final behavior. That worked because primaries are done a few states at a time, and because all the people being modeled were Democrats.<\/p>\n

It turns out that white people vary a lot across the country with how many per state are assholes. I think there is some variation among Hispanics as well, but African Americans are pretty consistent. So, here, I combined ethnicity with a “Romney Index” indicating how many people in a given state voted for Romney against Obama.
\n—-LATEST PREDICTION HERE CLICK HERE—-<\/strong><\/a><\/p>\n

I then put down the poll numbers, the averages of the last several polls, from RCP, where available. I then ranked the results to knock out states with no polls. I then took out the middle, which included swing states, close states, etc. to use only the 23 most distinct states for which there were data to produce a multi variable regression model using “white”, “black”, “hispanic”, and “romney_index” as independent variables. The dependent variable was the poll value. In future iterations, that is what will change. I’ll do a more refined version of that.<\/p>\n

I then applied this formula to predict the breakdown between Clinton and Trump in the other ca. half of the states that are more ambiguous.<\/p>\n

The multiple R-squared for this model was 0.952, so that’s great. But, I was using only the values at the extreme, so I violated the law of homoscedasticity. But I don’t care about no stinking homoscedasticity, because I have only one data set, am predicting only one election, and I am basically using the regression model as a fancy fill in the blank formula. The fact that the R-squared is so high is great, were it low, I’d be in trouble, but its actual value is not important.<\/p>\n

I then took all the states where Trump gets over 50% of the vote and gave them to him. I then gave almost all the other states to Clinton, but I left out a few that were very close, to leave them as unknown. Even if all those unknowns go to Trump, however, the outcome is the same: Clinton wins. Trump loses.<\/p>\n

I’ll refine and revise again with more care given to the various parts of the model. I’d love to do this poll free, but not sure if that is possible.<\/p>\n

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\nThe final output data are spewed onto 270 to win.<\/a><\/p>\n

NEXT PREDICTION<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

I’ve made my first stab at a prediction for the electoral college outcome for the US Presidential race, 2016. I use a roughly similar methodology as I did to accurately predict most of the Democratic primaries. However, since primaries are different from a general, the methodology had to be adapted. For the primaries, I eventually … Continue reading Who will win the presidential race?<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":23068,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[2219,774,772,2220,2221],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p5fhV1-602","jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/posts\/23066"}],"collection":[{"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/comments?post=23066"}],"version-history":[{"count":0,"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/posts\/23066\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gregladen.com\/blog\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/media?parent=23066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/categories?post=23066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gregladen.com\/blog\/wp-json\/wp\/v2\/tags?post=23066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}