In this UN data set, female life expectancy in years (lifeexpf) is regressed onto birthrate (birthrat=number of live births per 1,000 of the population), doctors (docs=number of doctors per 10,000 of the population), natural log of phones (Inphones; where phones=number of phones per 100 of the population), and percentage urbanized (urban). Using the following Minitab output and graphs: a) Interpret the coefficient of docs. Test the significance of the variable showing all your work. b) Interpret the graphs, writing any comments under each graph. If the graph is used for testing any regression assumptions, please state what the assumptions are. Do you have any recommendations for improving the model? Write any recommendations here: c) Is this model usable? Perform any tests necessary to show why or why not and show your work. d) What can you say about the prediction for female life expectancy and the prediction interval shown on the Minitab out put? Regression Analysis: lifeexpf versus birthrat, docs, Inphones, urban The regression equation is lifeexpf - 71.7 - 0.325 birthrat - 0.0000 does + 3.15 Inphones + 0.0219 urban 112 cases used, 10 cases contain missing values Predictor Coef SE Coef T P VIF Constant 71. 668 2. 613 27.43 0.000 birthrat -0. 32548 0.06032 -5. 40 0.000 3. 624 docs -0. 00002 0. 06900 3. 484 Inphones 3. 1495 0. 4674 6.74 0.000 4. 783 urban 0. 02189 0. 02692 0. 81 0. 418 2.534 5 - 4.37467 R-Sq - 85.3% R-Sq(adj ) - 84.7% Analysis of Variance Source DE SS MS p-value Regression 11843.9 0.000 Residual Error 107 2047.7 19.1 Total 111 13891 .7 Source DF Seq SS birthrat 10282.9 docs 320.0 Normal Probability Plot of the Residuals Histogram of the Residuals PercentResiduals Versus the Fitted Values [response is Ffeexpl! 10 Residual -10 -15 50 55 50 65 70 75 80 85 Fitted Value Inphones 1228. 4 urban 12.7 Unusual Observations obs birthrat lifeexpf Fit SE Fit Residual St Resid 14 46.0 47.000 56. 456 0. 670 -9. 456 -2. 19R 17 42.0 41.000 53. 301 0 . 854 -12. 301 -2. 87R 19 28.0 56.000 64. 945 0.766 -8 . 945 -2. 08R 49 33.0 75.000 65.130 0.439 9. 870 2.27R 62 33.0 74.000 64. 666 0.430 9.334 2. 14R 80 29.0 67.000 57.332 1.247 9 . 668 2. 31R 81 44.0 43.000 53. 053 0.789 -10.053 -2. 34R 83 40 .0 48.000 54. 714 2.192 -6. 714 -1 . 77 x 8 8 21.0 74.000 65.340 1.238 8 . 660 2. 06R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Predicted Values for New Observations New Obs Fit SE Fit 954 CI 95% PI 1 58. 368 4. 580 (49.289, 67.447) (45. 813, 70.924) XX denotes a point that is an extreme outlier in the predictors. Values of Predictors for New Observations New Obs birthrat does Inphones urban 75.0 50.0 3.00 76.0 Scatterplot of lifeexpf vs birthrat, docs, Inphones, urban birthrat docs 70 60 50 40 lifeexpf 20 40 60 10 20 30 40 Inphones urban 80 70 60 50 an 25 50 75 100