By Herschel Smith
1. Our model explains 96% of county-level variance in Trump’s two-party vote share with four demographic variables (non-college white, college-educated white, black and hispanic) and one historical variable (the average of county-level GOP two-party presidential vote share, 2004-2016). All five variables are highly significant. This reinforces the conclusion that the model is generally a very strong predictor of vote shares, and so deviations from it should be considered surprising.
2. Under conservative assumptions, regression analysis shows Trump ought to have won AZ, GA, NV, PA, WI.
3. Every one of the contested states shows a larger predicted vote share for Trump than what he actually received. This is surprising, because in any set of observations, random chance might expect some predictions to favor Biden, but none do. In Georgia and Arizona, the model does not predict a narrow race, but a decisive Trump victory; the size of the anomaly is (much) larger than the reported margin of victory.
4. The model also performs well in battleground states that have not been contested, and thus where the election was presumably clean. Every one of these is correctly predicted, including both battleground states that voted for Trump (e.g. Ohio, Florida) and those that voted for Biden (e.g. New Hampshire). Indeed, there are no states that Trump won which the model predicts should have been won by Biden. Meanwhile, the errors in the model are constructed to average to zero, so the model cannot favor one candidate over the other. Instead, it reveals the places where actual outcomes differ the most from our predictions.
5. The model is robust to alternative specifications of the regression formula and weighting.
6. The model places the burden of proof on fraud skeptics to explain why nearly all the states where fraud has been alleged, and only those states, have results inconsistent with statistical trends in the rest of the country.
7. Our model highlights the importance of a systematic comparison of all counties in the US when trying to understand whether the contested states are actually unusual. Simply picking isolated comparison cities, or one-off comparisons to past elections, is a very inferior way of doing the comparison. This model takes this base intuition (which is actually good), but greatly improves it by making the comparison systematic. The fact that the contested states are mostly predicted to have been won by Trump using simple but powerful demographic models further adds weight to the existing evidence that these outcomes may have been altered by fraud.
Data and full analysis at the link. Joe Biden is a fraud. The constitution is a dead document. The federal government is completely illegitimate, front to back, side to side, top to bottom. You do not live in a republic. The USA should be referred to as the former USA, or FUSA.
