Model Summary S R-sq R-sq(adj) 1.21758 53.36% 52.39% Analysis of Variance Source DF SS MS F P Regression 1 81.424 81.4235 54.92 0.000 Error 48 71.160 1.4825 Total 49 152.583 Fitted Line Plot LifeExp = 84.19 - 0.4841 Poverty 82 S 1.21758 R-Sq 53. 4% 81 R-Sq(adj) 52. 4% 80 79 LifeExp 78 77 76 75 741 6 8 10 12 14 16 18 20 Poverty15. What percentage of the variation in life expectancies across the 50 states can be explained by differences in the states' poverty rates? 16. From your scatterplot and residual plot, does it appear that linear regression is appropriate for these data? Show the scatterplot and residual plot, and write a few sentences explaining your answer. 17. What would the regression predict to be the life expectancy in California? How does this compare to the actual life expectancy in California? Versus Fits (response is LifeExp) 4 W N Residual 0 -1 -2 74 75 76 77 78 79 80 81 Fitted Value