SPSS Problem 1 on page 358

1. Explore the relationship between the number of siblings a respondent has (SIBS) and his or her number of children (CHILDS).

1. Construct a scatterplot of these two variables in SPSS, and place the best fit linear regression line on the scatterplot. Describe the relationship between the number of siblings a respondent has (IV) and the number of his or her children (DV).
2. Calculate the regression equation predicting CHILDS with SIBS. What are the intercept and the slope? What are the coefficient of determination and the correlation coefficient?
3. What is the predicted number of children for someone with three siblings?
4. What is the predicted number of children for someone without any siblings?

SPSS Problem 4 on page 359

4. Use the same variables as in Exercise 1, but do the analysis separately for married and divorced respondents. Begin by locating the variable MARITAL. Click Data, Split File, and then select Organize Output by Groups. Insert MARITAL into the box and click OK. SPSS will split your results by marital status.

1. Is there any difference between the regression equations for married and divorced respondents?
2. What is the predicted number of children for married and divorced respondents with the following number of siblings: one sibling, four siblings, and seven siblings?
3. What differences, if any, do you find?

Exercise 10 on pages 367-368

10. In Exercise 6, we examined the relationship between years of education and hours of television watched per day. We saw that as education increases, hours of television viewing decreases. The number of children a family has could also affect how much television is viewed per day. Having children may lead to more shared and supervised viewing and thus increases the number of viewing hours. The following SPSS output displays the relationship between television viewing (measured in hours per day) and both education (measured in years) and number of children. We hypothesize that whereas more education may lead to

less viewing, the number of children has the opposite effect: Having more children will result in more hours of viewing per day.

1. What is the \(b\) coefficient for education? For number of children? Interpret each coefficient. Is the relationship between each independent variable and hours of viewing as hypothesized?
2. Using the multiple regression equation with both education and number of children as independent variables, calculate the number of hours of television viewing for a person with 16 years of education and two children. Using the equation from Exercise 6, how do the results compare between a person with 16 years of education (number of children not included in the equation) and a person with 16 years of education with two children?
3. Compare the \(r^{2}\) value from Exercise 6 with the \(r^{2}\) value from this regression. Does using education and number of children jointly reduce the amount of error involved in predicting hours of television viewed per day?

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