In problem 13.1, data regarding voter turnout in five cities was presented. For the sake of convenience, the data for three of the variables are presented again here along with descriptive statistics and zero-order correlations.

a. Compute the partial correlation coefficient for the relationship between turnout (Y) and unemployment (X) while controlling for the effect of negative advertising (Z). What effect does this control variable have on the bivariate relationship? Is the relationship between turnout and unemployment direct?
b. Compute the partial correlation coefficient for the relationship between turnout (Y) and negative advertising (X) while controlling for the effect of unemployment (Z). What effect does this have on the bivariate relationship? Is the relationship between turnout and negative advertising direct?
c. Find the unstandardized multiple regression equation with unemployment (X1) and negative ads (X2) as the independent variables. What turnout would be expected in a city in which the unemployment rate was 10% and 75% of the campaign ads were negative? (Use Formulas 14.4 and 14.5 to compute the partial slopes and then use Formula 14.6 to find a, the Y intercept. The regression line is stated in Formula 14.3. Substitute 10 for X1 and 75 for X2 to compute predicted Y.)
d. Compute beta-weights for each independent variable.
Which has the stronger impact on turnout?
e. Compute the multiple correlation coefficient (R) and the coefficient of multiple determination (R2). How much of the variance in voter turnout is explained by the two independent variables?

Unemployment % Negative Turnout Rate Ads 5 50 63 60 65 68 70 53 48 55.8 10 Mean 63.6 5.5 8.2 1.7 5.3 Unemployment Negative Rate Ads -0.87 -0.70 Turnout 0.95 nemployment Rate