C8. Research on social mobility, status, and educational attainment has provided convincing evidence on the relationship between parents' and children's socioeconomic achievement. For this exercise, refer to the scatterplot and regression output from a GSS 2018 subsample to describe the relationship between mother's education and respondent's education. R' Linear = 0.193 Highest year of school completed Highest year school completed, mother Description Scatterplot of Respondent Level of Education by Mother's Level of Education Model Summary Adjusted R Sid. Error of Model R R Square Square the Estimate .411 .169 .168 2.746 a. Predictors: (Constant), Mothers highest degree ANOVA Sum of Model quares Mean Square Sig Regression 1563.928 1563.928 207.348 .000 Residual 7693.360 1020 7.543 Total 9257.288 1021 a. Dependent Variable: Highest year of school completed b. Predictors: (Constant), Mothers highest degree Coefficients Standardized Unstandardized Coefficients Coefficients Model Sid. Error Beta sig. (Constant) 12.433 124 100.037 .000 Mothers highest degree 1.090 .076 .411 14.400 .000 a. Dependent Variable: Highest year of school completed Description Linear Regression Output Specifying the Relationship Between Respondent's Education by Mother's EducationC10. 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. a. 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? b. 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? c. Compare the / value from Exercise 6 with the R- 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? Model Summary Adjusted R Std. Error of Model R Square Square the Estimate .213 .046 .043 3.173 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA Sum of Model Square of Mean Square Sig Regression 351.146 175.573 17.439 Residual 7359.542 731 10.068 Total 7710.688 733 a. Dependent Variable: Hours per day watching TV b. Predictors: (Constant), Number of children, Highest year of school completed Coefficients* Standardized Unstandardized Coefficients Coefficients Model Std. Error Beta Sig. Constant 5.596 .593 9.438 .000 Highest year of school -.201 039 -.190 -5.105 .000 completed Number of children .118 .071 062 1.657 .098 a, Dependent Variable: Hours per day watching TV Description Multiple Regression Output Specifying the Relationship Between Education, Number of Children, and Hours Spent per Day Watching Television