Sir..I am on stuck with following questions:-
In problem 13.1, data regarding voter turnout in five cities were 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. Unemployment City Turnout Rate % Negative Ads A 55 5 60 B 60 CO 63 C 65 9 55 68 9 53 m 70 10 48 Mean = 63.6 8.2 55.8 S= 5.5 1.7 5.3 Unemployment Negative Rate Ads Turnout 0.95 -0.87 Unemployment Rate -0.70 a. Compute the partial correlation coefficient for the relationship between turnout (Y) and unemployment rate (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? (HINT: Use Formula 15.1.) b. Compute the partial correlation coefficient for the relationship between turnout (Y) and negative advertising (X) while controlling for the effect of unemployment rate (Z). What effect does this control variable have on the bivariate relationship? Is the relationship between turnout and negative advertising direct? (HINT: Use Formula 15.1. You will need this partial correlation to compute the multiple correlation coefficient.) C. Find the unstandardized multiple regression equation with turnout as the dependent variable and unemployment (X,) 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 15.4 and 15.5 to compute the partial slopes and then use Formula 15.6 to find the Y intercept, a. The regression line is stated in Formula 15.3. Substitute 10 for X, 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 15.7 and 15.8 to calculate the beta-weights.) e. Compute the multiple correlation coefficient (R) and the coefficient of multiple determination (R-). How much of the variance in voter turnout is explained by the two independent variables? (HINT: Use Formula 15. 11. You calculated ry 2.1 in part b of this problem.) f. Write a paragraph summarizing your conclusions about the relationships among these three variables. Problem 13.1: Why does voter turnout vary from election to election? For municipal elections in five different cities, information has been gathered on the percentage of citizens who voted (the dependent variable) and three different independent variables: unemployment rate, average years of education for the city, and the percentage of all political ads that used "negative campaigning" (personal attacks, negative portrayals of the opponent's record, etc.). For each relationship between turnout (Y) and the independent variables: a. Compute the slope (b) and find the Y intercept (a). (HINT: Remember to compute b before computing a. A computing table such as Table 13.3 is highly recommended.) b. State the least-squares regression line and predict the voter turnout for a city in which the unemployment rate was 12%, a city in which the average years of schooling was 11, and an election in which 90% of the ads were negative. c. Computer and 12. (HINT: A computing table such as Table 13.3 is highly recommended. If you constructed one for computing b, you already have most of the quantities you will need to solve for r.) d. Assume these cities are a random sample and conduct a test of significance for each relationship. e. Describe the strength and direction of each relationship in a sentence or two. Which (if any) relationships were significant? Which of the independent variables had the strongest effect on turnout? Average Years of Percentage Turnout Unemployment School of Negative City Rate (X,) (X2) Ads (X,) 55 11.9 60 CO 12.1 63 12.7 55 12.8 53 m TO 13.0 AB
