An interesting machine learning problem: Can one figure out the relationship between the popular vote margin, voter turnout, and the percentage of electoral college votes a candidate wins? Going back to the election of John Quincy Adams, the raw data looks like this: Electoral College Party Popular vote Margin (%) Turnout Percentage of EC John […]
Tag: Clinton
Further to my original post on using physics to predict the outcome of the 2016 US Presidential elections, I have now written a cloud-based app using the powerful Wolfram Cloud to pull the most recent polling data on the web from The HuffPost Pollster, which “tracks thousands of public polls to give you the latest […]
Using the Monte Carlo techniques I have described in earlier posts, I ran several simulations today to try to predict who will win the 2016 Michigan primaries. Here is what I found: For the Republican primaries, I predict: Trump: 89.64% chance of winning Cruz: 5.01% chance of winning Kasich: 3.29% chance of winning Rubio: 2.06% […]
A short post by me today. I wanted to look at the which states are important in winning the national election. Looking at the last 14 presidential elections, I generated the following correlation plot: For those not familiar with how correlation plots work, the number bar on the right-hand-side of the graph indicates the […]
A great deal of noise has been made in the previous weeks about the surge in the polls of Donald Trump and Bernie Sanders. This has led some people to question whether Hillary Clinton will actually end up being the Democratic party nominee in 2016. This was further evidenced by the fact that Sanders is […]
Relativity Digest 