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For PA Counties |
PA Counties
|
Coefficients
|
Standard Error
|
t Stat
|
P-value
|
Lower 95%
|
Upper 95%
|
Intercept
|
10.76
|
7.08
|
1.52
|
0.13
|
-3.38
|
24.91
|
% Bachelors
|
-0.84
|
0.08
|
-10.87
|
3.57E-16
|
-1.00
|
-0.69
|
% white
|
0.78
|
0.07
|
11.34
|
5.93E-17
|
0.64
|
0.91
|
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For US States |
For the model for the states there were three three significant variables accounting for 78.5% of the variability: % bachelors, % white, and % uninsured. For every 1% increase in the % bachelors there was a 1.11% decrease in the predicted Trump %. Likewise there was a 0.31% increase for Trump for a 1% increase in % white and a 0.74% increase for a 1% increase in the % uninsured. The chart above shows few points were poorly fit by the model. The concentration of hate groups was no longer significant when % white was added to the model.
US States
|
Coefficients
|
Standard Error
|
t Stat
|
P-value
|
Lower 95%
|
Upper 95%
|
Intercept
|
51.55
|
8.92
|
5.78
|
5.75E-07
|
33.61
|
69.48
|
% bachelors
|
-1.11
|
0.15
|
-7.55
|
1.2E-09
|
-1.41
|
-0.82
|
% White
|
0.31
|
0.06
|
4.95
|
1.01E-05
|
0.18
|
0.43
|
% uninsured
|
0.74
|
0.26
|
2.86
|
0.006319
|
0.22
|
1.26
|
The addition of % white population in the states of the US and the counties of PA has greatly improved the predictive power of the model and reduced the effect of outliers in the model. At the state level the effect of the uninsured shows that an increase in the uninsured predicts an increase in Trump's vote. This is troubling given that the health care reform law considered by congress will likely increase the uninsured.
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