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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 (number of children not included in the equation) and a person with 16 years education with two children? years of education of c. Compare ther 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? Multiple Regression Output Specifying the Relationship Between Education, Number of Children, and Hours Spent per Day Watching Television Model Summary Adjusted R Square Std. Error of the Estimate Model R Square 1 .213 .046 .043 3.173 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA Sum of Model Squares df Mean Square Sig. Regression 175.573 .000b 1 351.146 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 Coefficients Unstandardized Coefficients Model B. Std. Error Beta Sig. 1 (Constant) 5.596 .593 9.438 .000 Highest year of school completed -.201 .039 -.190 -5.105 .000 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV

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 (number of children not included in the equation) and a person with 16 years education with two children? years of education of c. Compare ther 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? Multiple Regression Output Specifying the Relationship Between Education, Number of Children, and Hours Spent per Day Watching Television Model Summary Adjusted R Square Std. Error of the Estimate Model R Square 1 .213 .046 .043 3.173 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA Sum of Model Squares df Mean Square Sig. Regression 175.573 .000b 1 351.146 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 Coefficients Unstandardized Coefficients Model B. Std. Error Beta Sig. 1 (Constant) 5.596 .593 9.438 .000 Highest year of school completed -.201 .039 -.190 -5.105 .000 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 15PPS
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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 r value from Exercise 6 with the R² 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? Multiple Regression Output Specifying the Relationship Between Education, Number of Children, and Hours Spent per Day Watching Television Model Summary Adjusted R Square Std. Error of the Estimate Model R R Square 1 .213a .046 .043 3.173 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression 351.146 2. 175.573 17.439 .000b 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 Coefficients Unstandardized Coefficients Model Std. Error Beta Sig. 1 (Constant) 5.596 .593 9.438 .000 Highest year of school completed -.201 .039 -.190 -5.105 .000 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV
Transcribed Image Text: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 r value from Exercise 6 with the R² 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? Multiple Regression Output Specifying the Relationship Between Education, Number of Children, and Hours Spent per Day Watching Television Model Summary Adjusted R Square Std. Error of the Estimate Model R R Square 1 .213a .046 .043 3.173 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression 351.146 2. 175.573 17.439 .000b 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 Coefficients Unstandardized Coefficients Model Std. Error Beta Sig. 1 (Constant) 5.596 .593 9.438 .000 Highest year of school completed -.201 .039 -.190 -5.105 .000 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV
C10. In Exercise 6, we examined the relationship between vears of education and hours ot 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. Sig. ,000 .000 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
Transcribed Image Text:C10. In Exercise 6, we examined the relationship between vears of education and hours ot 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. Sig. ,000 .000 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
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