Learning from Last Year (Part III)

As promised, this is the last edition of the “learning from last year” series (see parts I and II). Here, I’m just going to post what the two election models mentioned in Part II (the linear prediction and probit models) would have predicted, versus what actually happened. In both cases, the models got 14/15 candidates right, but they each missed a different one (and falsely predicted a different candidate as well). As a methodological note, the way that these predictions were created was by generating the probabilities predicted by each model and assigning any value over .5 as a “win” (i.e. successful election) and anything less than .5 as a “loss” (unsuccessful campaign). Without further ado, let me begin:

This is a table of the predicted wins, losses, and probabilities for each model.
So who are these people (unless we have a senator named “observation 1?”)?

Well, here are the results that matter (see below for the full results): both models correctly chose Zietsman, Warma, Wharton, Wanyoike, Ali, Cruz, Johnson, Limon, Sivaram, Young, Gao, Singleton, and Creasman. However, the linear model falsely predicted the election of Willmott, while missing the actual election of Jackson. The probit model falsely predicted the election of Henry while also missing the actual election of Jackson. Not too shabby.

Full results (I apologize for the awkward appearance: I blame the underlying blog platform):

1Anton Zietsman
2Zachary Warma
3Kelsei Wharton
4Brian Wanyoike
5Matthew Willmott
6Mohammad Ali
7Michael Cruz
8Zachary Johnson
9Daniel Limon
10Varun Sivaram
11Dean Young
12Shelley Gao
13Steven Q Singleton
14Adam Creasman
15Cameron Henry
16Janet Bill
17Alex Katz
18Alan Guo
19Jonathan Gelbart
20Lee Jackson
21Benjamin Jensen
22Otis Reid
23Rafael Vasquez
24Raillan Brooks
25Shinjini Kundu
26Bryce Kam
27Ben Laufer
28Howard Tan
29Erik Donhowe
30Matt Miller
31Steven Morris
32Sam King
33Monzurat Oni
34Ruthie Arbeiter

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