Georgia’s Gratuitous General Electoral Laws
Table 4 Correlation Analysis and O.L.S. Regression
two variables were not included in order to avoid the problem of multicollinearity. Both the percentage of the vote for Chambliss and the percentage of the vote for Obama in the general election are good predictors of the dependent variable, as will be seen in the analysis of Figure 2 and Figure 3, but they are also excellent predictors of each other. Had the aforementioned two variables been included in the multiple regression analysis the data would have produced an r2= .983. The Ordinary Least Square (OLS) coefficients and standard errors for the variables included in the multiple regression analysis are displayed in Table 4. The r2 of the data is .831, indicating that 83.1% of the variance in the percent of the vote for Chambliss is accounted for by the independent variables included in the analysis. The OSL regression coefficients for the variables of the percent African American and population density are statistically significant at p<.01. Therefore, the null hypotheses for both H1 and H4 can be rejected. The regression coefficient for Percent African American indicates that for every percentage increase in the African American population, the percentage of the vote for Chambliss in the runoff decreases by .663%. The regression coefficient for population density indicates that very every additional person per square mile in a county, the percentage of the vote for Chambliss decreases by .009%. A statistically significant relationship with the dependent variable was not found for any of the other independent variables included in the multivariate model. Therefore, for H2, H3, H5, and HÂŹ8 we must fail to reject the null hypotheses. It should be noted that the bivariate correlation analysis reveals that the variable of percent female is statistically significant. However, the significance seen in the bivariate analysis is lost in the
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