and see Section 17.2.)

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? (HINT: Use Formula PS 17.1 and see Section 17.2. You will need this partial correlation to compute the multiple correlation coefficient.)

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?

(HINT: Use Formulas 17.4 and 17.5 to compute the partial slopes and then use Formula 17.6 to find

a, the Y intercept. The regression line is stated in Formula 17.3. 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? (HINT: Use Formulas 17.7 and 17.8 to calculate the beta-weights.)

e. Compute the multiple correlation coefficient

(R ) and the coefficient of multiple determination (R 2 ). How much of the variance in voter turnout is explained by the two independent variables? (HINT: Use Formula 17.11. You calculated r 2 y 2.1 in part b of this problem.)

f. Write a paragraph summarizing your conclusions about the relationships among these three variables.