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.

1. What is the b coefficient for education? For age? Interpret each coefficient. Is the relationship between education and Internet hours as hypothesized in our chapter example? For age and Internet hours?

2. Using the multiple regression equation with both education and age as independent variables, calculate the number of Internet hours per week for a person with 16 years of education and 55 years of age.

3. Using the standardized multiple regression equation, identify which independent variable has the strongest effect on Internet hours per week.

4. Interpret the multiple coefficient of determination.

5. Interpret each correlation coefficient (based on the correlation matrix).

Description Multiple Regression Output for Internet Hours per Week, Educational Attainment, and Age

Correlation Matrix for Internet Hours per Week, Educational Attainment, and Age

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Model Summary Model 1 R .319 R Square .101 Adjusted R Square .099 Std. Error of the Estimate 16.402 a. Predictors: (Constant), Age of respondent, Highest year of school completed Model ANOVA Sum of Squares df Regression Residual 19173.948 2 Total 169748.495 188922.443 631 633 Mean Square 9586.974 269.015 F 35.637 Sig. .000b a. Dependent Variable: www.hours per week b. Predictors: (Constant), Age of respondent, Highest year of school completed Coefficients Unstandardized Coefficients Model 1 (Constant) Highest year of school completed Age of respondent B 21.827 .515 -.309 a. Dependent Variable: Www.hours per week Std. Error 3.746 .237 .037 Standardized Coefficients Beta t Sig. .082 5.827 2.171 .000 .030 -.311 -8.235 .000