{"id":3314,"date":"2017-05-23T18:18:27","date_gmt":"2017-05-23T16:18:27","guid":{"rendered":"https:\/\/geekosas.com\/?p=3314"},"modified":"2026-05-23T18:19:01","modified_gmt":"2026-05-23T16:19:01","slug":"estimation-of-vote-distribution-in-the-second-round-of-the-chilean-presidential-elections-2017","status":"publish","type":"post","link":"https:\/\/geekosas.com\/index.php\/2017\/05\/23\/estimation-of-vote-distribution-in-the-second-round-of-the-chilean-presidential-elections-2017\/","title":{"rendered":"Estimation of vote distribution in the second round of the Chilean presidential elections 2017"},"content":{"rendered":"

This article was made in a hurry and with little data; the definitive one is at Who voted for each candidate?<\/a><\/p>\n

After the first round of the Chilean presidential elections, it is traditional for each candidate to begin "auctioning off" the votes of their followers to the candidate they feel most aligned with. In reality, votes cannot be "transferred" and since the vote is secret, no one knows who everyone voted for. That is why I decided to use data science to decipher how the votes from the first round were distributed to the second round.<\/p>\n

As with any model, assumptions must be made, and in this case the key assumption we will make is that the votes from the first round were distributed in the same way across all regions in the second round (I should possibly do it by polling station or commune, but the complete database is not yet available).<\/p>\n

What we will do is perform a linear regression with constraints of the type beta*X = y, where:<\/p>\n