Measurements of smaller populations should have larger uncertainty and error bars - and this is exactly what we see in New Hampshire, the smallest state to be covered so far on this blog series. Aside from a spectacular win by George H. W. Bush in 1988, New Hampshire has fluttered back and forth from republican-leaning in 1992 to democratic-leaning in 1996, back to the right in 2000, and finally back to the left for the following 3 elections since. I'll interpret this to mean that the Dukakis campaign probably abandoned all hope of winning there, and focused instead on other states. Subsequent campaigns probably focused more on New Hampshire, bringing it closer to the center.
We can see the somewhat ridiculous prediction by my naive linear model: an 8.9% advantage by the democrat, with rather large 4.6% error bars. Any real person looking at this data would predict that it'll probably be much closer, with perhaps a small democratic advantage (neglecting 1988 from my linear fit model - without any objective basis - gives a prediction of a smaller democratic advantage of 0.8-5.4%). My model could benefit from some way of weighting recent elections more heavily than long-ago elections. Maybe next time.
Coming up sometime soon (maybe) by popular demand: including the midterm election data in my prediction and analysis!
We can see the somewhat ridiculous prediction by my naive linear model: an 8.9% advantage by the democrat, with rather large 4.6% error bars. Any real person looking at this data would predict that it'll probably be much closer, with perhaps a small democratic advantage (neglecting 1988 from my linear fit model - without any objective basis - gives a prediction of a smaller democratic advantage of 0.8-5.4%). My model could benefit from some way of weighting recent elections more heavily than long-ago elections. Maybe next time.
Coming up sometime soon (maybe) by popular demand: including the midterm election data in my prediction and analysis!
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