We return to our chapter analysis of Internet hours per week (WWHR), educational attainment (EDUC), and respondent age (AGE), presenting the multiple regression model and correlation matrix based on GSS 2014 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).

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Model Summary Adjusted R Šquare Std. Error of the Estimate 15.406 R R Square .065 Model 255 .061 a. Predictors: (Constant), age AGE OF RESPONDENT, educ HIGHEST YEAR OF SCHOOL COMPLETED ANOVA Sum of Squares Model df Mean Square Sig. Regression 6920.364 2 3460.182 14.579 000 Residual 99448.621 419 237.348 Total 106368.986 421 a. Dependent Variable: wwwhr Www HOURS PER WEEK b. Predictors: (Constant), age AGE OF RESPONDENT, educ HIGHEST YEAR OF SCHOOL COMPLETED Coefficients Standardized Coefficients Unstandardized Coefficients B Model Std. Error Beta Sig. 1 (Constant) 14.395 4.202 3.426 .001 educ HIGHEST YEAR OF SCHOOL COMPLETED .598 .263 .108 2.271 .024 age AGE OF RÉSPONDENT -.236 .047 -.241 -5.080 .000 a. Dependent Variable: wwwhr WwW HOURS PER WEEK