Here we will compare data sets relating to the percentage of votes which were cast for Hillary Clinton in the 2016 Presidential Election by comparing a Bivariate Table analysis to Multiple Regression analysis.
Correlation matrix:

When we compare the Bivariate results with the regression analysis, we still see a negative relationship with the percentage of the population that is white with respect to the percentage of votes for Clinton. The relationships for Graduate degree, Poverty, and percent of Management Professionals remain pretty constant as well. We can see a direct Inverse relationship where percentage of the White population and Percentage with Graduate degrees, is concerned. A relatively Strong relationship between % white and votes for Clinton, and a strong positive relationship in the case of % with Graduate degrees and votes for Clinton. Just as in the bivariate analysis we did in week 13, we see in the multiple regression analysis, and especially in the TStat that their scores are about equally  in inverse directions. This tells me that because these two variables remain statistically significant in both types of analysis, that they are strong indicators to make predictions about the Independent variable (Clinton 2 Party vote).
Multiple linear regression results:
Dependent Variable: d2pty Independent Variable(s): gtba, whitepct, poverty, mngmnt_Prof, service d2pty = 45.246999 + 2.0364789 gtba + 0.41771422 whitepct + 0.15391639 poverty + 0.082936095 mngmnt_Prof + 0.76929017 service Parameter estimates:
Analysis of variance table for multiple regression model:
Summary of fit: Root MSE: 10.620915 Rsquared: 0.5844 Rsquared (adjusted): 0.5775 
So, if we compare the bivariate table to the multiple regression analysis we can see the same correlations, in terms of there being two variables which appear to stand out. These variables are again the percentage of the population that is white, and those who have graduate degrees. According to the two Multiple Regression tables, in which the second analysis includes the Gini Coefficient, according to the RSquared and the RSquared (adjusted) we are able to explain about 58% of the correlations with the dependant variable (2 party vote)  Clinton.
In general, an Ftest in regression compares the fits of different linear models. Unlike ttests that can assess only one regression coefficient at a time, the Ftest can assess multiple coefficients simultaneously.
The Ftest of the overall significance is a specific form of the Ftest. It compares a model with no predictors to the model that you specify. A regression model that contains no predictors is also known as an interceptonly model.
The hypotheses for the Ftest of the overall significance are as follows:
 Null hypothesis: The fit of the interceptonly model and your model are equal.
 Alternative hypothesis: The fit of the interceptonly model is significantly reduced compared to your model.
Based on this definition, it would seem that when you add the GiniCoefficient as an additional Independent variable, it changes the FStat, however the PValue appears to remain the same, and indicates statistical significance.
The only variable which change significantly in terms of Tstat is Poverty.
My observations from a previous analysis, which stated that the overall seemingl most significant Independent variables for prediction outcomes for the Dependent variable remain, both Percentage of White Population, and the percentage of respondents with Graduate degrees in a given county most strongly determines the percentage of votes for Hillary Clinton in the 2016 Presidential Election.
I am still surprised that those living below the poverty line were not more apt to vote for Hillary, nor were those who are in the Service Industry.
As far as fit of the model, it seems like the two versions of the Multiple Regression model are a relatively accurate fit, but there may be some further analysis necessary to determine which of these variables explains the lions' share of the the percentage of the two party vote which Hillary Clinton received in the 2016 election.
Multiple linear regression results:
Dependent Variable: d2pty Independent Variable(s): gtba, whitepct, poverty, gini_coefficient, mngmnt_Prof, service d2pty = 50.184491 + 2.0734791 gtba + 0.41806189 whitepct + 0.10141004 poverty + 14.078804 gini_coefficient + 0.083285106 mngmnt_Prof + 0.77626859 service Parameter estimates:
Analysis of variance table for multiple regression model:
Summary of fit: Root MSE: 10.63222 Rsquared: 0.5849 Rsquared (adjusted): 0.5766 
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