Chapter 12

Occupational prestige scores for a sample of fathers and their oldest son and oldest daughter are presented in the following table. Analyze the relationship between father's and son's prestige and the relationship between father's and daughter's prestige. For each relationship, a. Compute the slope $(b)$ and find the $Y$ intercept $(a)$. b. State the least-squares regression line. What prestige score would you predict for a son whose father had a prestige score of $72?$ What prestige score would you predict for a daughter whose father had a prestige score of 72 ? c. Compute $r$ and $r^2$. d. Describe the strength and direction of the relationships in a sentence or two. Does the occupational prestige of the father have an impact on his children? Does it have the same impact for daughters as it does for sons?

SOC Load the Intl-Pop data set and get scatterplots for the independent variable GNIcap (Gross National Income per capita, a measure of affluence) and each of these dependent variables: BirthRate (birth rate or number of births per 1000 population), DthRate (death rate or number of deaths per 1000 population), and $L$ ifeExp (life expectancy or the average number of years of life a newborn infant can expect to live). \- Click Graphs, Legacy Dialog, and Scatter/Dot. \- On the "Scatter/Dot" window, click Simple Scatter and then click Define. \- On the "Simple Scatterplot" window, Find BrthRate in the list of variables and move the variable name to the "Y Axis:" box. \- Find GNIcap and click the arrow to move the variable name to the "X Axis:" box. I- Click OK and the scatterplot will be sent to the SPSS output window. \- To add the regression line to the graph, \- Click anywhere on the graph and the "Chart Editor" window will appear. \- Click the Elements command at the top of the window and, in the drop-down menu, click Fit Line at Total. \- A new window will open. In the "Fit Method" panel, click Linear and then click Close. \- Close the "Chart Editor" window to return to the Output window of SPSS. \- Repeat the Graphs, Legacy Dialog, and Scatter/ Dot commands and, when you get to the "Simple Scatterplot" window, substitute DthRate for BirthRate. Repeat the instructions above to get a scatterplot for DthRate and GNIcap. \- Repeat the Graphs, Legacy Dialog, and Scatter/ Dot commands once more and substitute $LifeExp$ for DthRate in the "Simple Scatterplot" window. Repeat the instructions above to get a scatterplot for LifeExp and GNIcap. a. Describe each of these graphs in terms of the direction and strength of the relationship they display. Use the regression line and the spread of the dots around the line to make these determinations. b. Do any of these scatterplots violate the assumption of linearity? How?

SOC Follow up on problem $12.11$ by finding the regression coefficients for the relationship between cell phone use and urbanization. Use the Intl-Pop dataset and the Regression command. \- Load the Intl-Pop database. \- From the main menu bar, click Analyze, Regression, and Linear. \- On the "Linear Regression" screen, place CellPhones in the "Dependent" window and Urban in the "Independent" window. \- Click OK and results will be printed to the Output screen. \- The "Coefficients" box displays the value of $a$ and $b$ under "Unstandardized Coefficients." Look in the column labeled $\mathrm{B}$. The value in the first row is $a$ and the entry in the second row is $b$. a. State the regression equation with the values for $a$ and $b$. b. Use the equation to predict cell phone use for nations that are $25\mathrm{\%}$ urbanized $(X=25),50\mathrm{\%}$ urbanized $(X=50),75\mathrm{\%}$ urbanized $(X=75)$, and $99\mathrm{\%}$ urbanized $(X=99)$. What happens to cell phone use as urbanization increases?

SOC/PS What are the correlates of voter turnout? Use the States database with Voters (percentage of the population voting for president in 2012 ) as the dependent variable and College (percent of the population with a college degree) and MdHHInc (median household income-a measure of affluence) as independent variables \- Click the SPSS icon on your desktop. \- Load the States database. \- From the main menu bar, click Analyze, Correlate, and Bivariate. \- In the "Bivariate Correlations" dialog box, find College, MdHHInc, and Voters and move them to the "Variables" window. \- Click OK and the output will be printed to the SPSS Output screen. Record the values of $r$ for each relationship. Calculate $r^2$ and record those values in the table as well. a. Write a summary of these relationships in which you note the strength and direction of each relationship. What percentage of the variance in voter turnout was explained by the education variable? By the affluence variable? b. Which of these relationships seems causal? Why?