1. (Dataset: States. Variables: vep16_turnout, clinton16.) An article of faith among Democratic Party strategists (and a source of apprehension among Republican strategists) is that high voter turnouts help Democratic candidates. Why should this be the case? According to the conventional wisdom, Democratic electorates are less likely to vote than are Republican voters. Thus, low turnouts naturally favor Republican candidates. As turnouts push higher, the reasoning goes, a larger number of potential Democratic voters will go to the polls, creating a better opportunity for Democratic candidates. Therefore, as turnouts go up, so should the Democratic percentage of the vote.¹⁴Use theAnalyze ?Regression ?Linear procedure to test this conventional wisdom. The States dataset contains vep16_turnout, the percentage of the state voting-eligible population that turned out to vote in the 2016 presidential election. This is the independent variable. Another variable, clinton16, the percentage of the vote cast for Democratic candidate Hillary Clinton, is the dependent variable.

A) Use linear regression analysis to examine the relationship between voter turnout and Hillary Clinton's vote share. Complete the following table.

Coefficient

Standard Error

t-ratio

P-value

% Voter turnout

?

?

?

?

Constant term

?

?

1.22

0.227

N

?

R-squared

?

Adjusted R-squared

?

B) Based on your results, the regression equation for estimating the percentage voting for Hillary Clinton is (fill in the blanks, put the constant, _cons, in the last blank)

Clinton Voter Percentage = _____________ Percentage Turnout + ____________.

C) Consider your findings in parts A and B. One may conclude that (complete one option)

??The conventional wisdom is correct because

??The conventional wisdom is incorrect because

DataSet

Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1 .266 071 052 10.07687 a. Predictors: (Constant), Percent turnout of voting eligible population in 2016 ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression 372.595 372.595 3.669 061 Residual 4874.084 48 101.543 Total 5246.678 49 a. Dependent Variable: Vote share for Clinton in 2016 election b. Predictors: (Constant), Percent turnout of voting eligible population in 2016ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression 372.595 372.595 3.669 061 Residual 4874.084 48 101.543 Total 5246.678 49 a. Dependent Variable: Vote share for Clinton in 2016 election b. Predictors: (Constant), Percent turnout of voting eligible population in 2016 Coefficients" Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta + Sig. (Constant) 17.123 13.988 1.224 227 Percent turnout of voting 432 226 266 1.916 .061 eligible population in 2016 a. Dependent Variable: Vote share for Clinton in 2016 election