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
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Question
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
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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