When adding the variables education (defined as % of population with a bachelor's degree or higher), % uninsured, and % change in child abuse cases with Trump % of the vote gives the following model with Philadelphia County included in the data set. The coefficients are the values for the regression equation:
Trump % = 105.86 - 1.31* (% Bachelors) - 1.85*(% Uninsured)
Coefficients
|
Standard Error
|
t Stat
|
P-value
|
Lower 95%
|
Upper 95%
|
|
Intercept
|
105.86
|
7.56
|
14.00
|
3.74E-21
|
90.75
|
120.96
|
Bachelors %
|
-1.31
|
0.13
|
-10.05
|
8.83E-15
|
-1.57
|
-1.05
|
Uninsured %
|
-1.85
|
0.77
|
-2.42
|
0.02
|
-3.38
|
-0.32
|
The scatterplot below shows the actual values for each county (in blue) and the predicted values (in orange) for the above equation. For example, Forest county had an actual % of the vote for Trump of 70.1% but the model predicts 79.3% for Trump with a predictor value of 8.3% uninsured and 8.5% with a bachelors degree or higher.
Philadelphia county is an outlier for the counties in Pennsylvania in much the same way as Washington, DC is for the other states. It had a big influence on the uninsured data so it was included in this model. Both Philadelphia and DC are demographically different from the surrounding counties and states respectively. The % of the white population in the counties and the states may be an important explanatory variable for the model.
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