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Unstandardized Coefficients B t Std. Error 1 (Constant) Sig. .000 12.423 .164 75.752 Mothers highest degree 1.117 .101 .416 .000 11.024 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed C10. 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 Standardized Coefficients Beta

Unstandardized Coefficients B t Std. Error 1 (Constant) Sig. .000 12.423 .164 75.752 Mothers highest degree 1.117 .101 .416 .000 11.024 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed C10. 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 Standardized Coefficients Beta

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Level of Education by Mother's Level of Education: Females Only Output Specifying the Relationship Between Respondent Model Summary Model R R Square Adjusted R Square .416b .173 Sed. Error of the Estimate 2.789 172 a. Respondents sex = FEMALE b. Predictors: (Constant), Mothers highest degree ANOVA Model df 1 Regression Residual Sum of Squares 945.352 4519.492 5464.844 Sig. .000* 1 Total 581 582 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed c. Predictors: (Constant), Mothers highest degree Coefficients ab Unstandardized Coefficients B t Model Std. Error Sig. .000 .000 75.752 1 (Constant) 12.423 .164 11.024 .416 Mothers highest degree 1.117 .101 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed C10. 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 viewing per day. children has the opposite effect: Having more children will result in more hours of a. What is the b coefficient for education? For number of children? Interpret each viewing as hypothesized? coefficient. Is the relationship between each independent variable and hours of 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 Mean Square 945.352 121.529 7.779 Standardized Coefficients Beta CHAPTER 12. REGRESSION AND CE
Transcribed Image Text:Level of Education by Mother's Level of Education: Females Only Output Specifying the Relationship Between Respondent Model Summary Model R R Square Adjusted R Square .416b .173 Sed. Error of the Estimate 2.789 172 a. Respondents sex = FEMALE b. Predictors: (Constant), Mothers highest degree ANOVA Model df 1 Regression Residual Sum of Squares 945.352 4519.492 5464.844 Sig. .000* 1 Total 581 582 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed c. Predictors: (Constant), Mothers highest degree Coefficients ab Unstandardized Coefficients B t Model Std. Error Sig. .000 .000 75.752 1 (Constant) 12.423 .164 11.024 .416 Mothers highest degree 1.117 .101 a. Respondents sex = FEMALE b. Dependent Variable: Highest year of school completed C10. 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 viewing per day. children has the opposite effect: Having more children will result in more hours of a. What is the b coefficient for education? For number of children? Interpret each viewing as hypothesized? coefficient. Is the relationship between each independent variable and hours of 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 Mean Square 945.352 121.529 7.779 Standardized Coefficients Beta CHAPTER 12. REGRESSION AND CE
BE VALU WINNER e 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 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 .213ª R Square .046 3.173 1 .043 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA F Mean Square Model Sig. .000b 17.439 Regression 1 Sum of Squares df 351.146 7359.542 7710.688 175.573 10.068 2 731 733 Residual Total a. Dependent Variable: Hours per day watching TV b. Predictors: (Constant), Number of children, Highest year of school completed Coefficients Standardized Coefficients Unstandardized Coefficients B Model Std. Error Beta t 1 (Constant) 5.596 .593 9.438 Highest year of school completed -.201 .039 -.190 -5.105 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV C11. We return to our chapter analysis of Internet hours per week (WWWHR), educational attainment (EDUC), and respondent age (AGE), presenting the multiple regression model and correlation matrix based on GSS 2018 data. relatio a. What is the b coefficient for education? For age? Interpret each coefficient. Is the Sig. .000 .000
Transcribed Image Text:BE VALU WINNER e 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 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 .213ª R Square .046 3.173 1 .043 a. Predictors: (Constant), Number of children, Highest year of school completed ANOVA F Mean Square Model Sig. .000b 17.439 Regression 1 Sum of Squares df 351.146 7359.542 7710.688 175.573 10.068 2 731 733 Residual Total a. Dependent Variable: Hours per day watching TV b. Predictors: (Constant), Number of children, Highest year of school completed Coefficients Standardized Coefficients Unstandardized Coefficients B Model Std. Error Beta t 1 (Constant) 5.596 .593 9.438 Highest year of school completed -.201 .039 -.190 -5.105 Number of children .118 .071 .062 1.657 .098 a. Dependent Variable: Hours per day watching TV C11. We return to our chapter analysis of Internet hours per week (WWWHR), educational attainment (EDUC), and respondent age (AGE), presenting the multiple regression model and correlation matrix based on GSS 2018 data. relatio a. What is the b coefficient for education? For age? Interpret each coefficient. Is the Sig. .000 .000
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