Using the same data, calculate the slope and y-intercept for the effect of age on income. Report and interpret the r and r2 values. In other words, how well does the line fit the data?

= Regression Variables Entered /Removed? Variables Variables Model Entered Removed Method 1 Age of \" Enter respondent a. Dependent Variable: Total family income b. All requested variables entered. Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1 .0187 .000 000 790261.428 a. Predictors: (Constant), Age of respondent ANOVA? Sum of Model Squares df Mean Square 1 Regression 7.969E+11 1 7.969E+11 Residual 2.521E+15 4037 6.245E+11 Total 2.522E+15 4038 a. Dependent Variable: Total family income b. Predictors: (Constant), Age of respondent Coefficients* 1.276 Standardized Unstandardized Coefficients Coefficients Model B Std. Error 1 (Constant) -2811.764 44868.714 Age of respondent 1343.107 1189.022 a. Dependent Variable: Total family income Beta .018 Sig. 259? t Sig. -.063 950 1.130 259