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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 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 6.74 0.000 Inphones 3.1495 0.4674 4.783 urban 0.02189 0.02692 0.81 0.418 2.534 s - 4.37467 R-Sq - 85.3% R-Sq(adj) - 84.74 Analysis of Variance p-value 0.000 Source DE ss MS Regression Residual Error 107 Total 4 11843.9 2047.7 19.1 111 13891.7 Source DF Seg ss 1 10282.9 birthrat docs 1 320.0 Normal Probabity Plotof the Residals Hgramhe Residls

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 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 6.74 0.000 Inphones 3.1495 0.4674 4.783 urban 0.02189 0.02692 0.81 0.418 2.534 s - 4.37467 R-Sq - 85.3% R-Sq(adj) - 84.74 Analysis of Variance p-value 0.000 Source DE ss MS Regression Residual Error 107 Total 4 11843.9 2047.7 19.1 111 13891.7 Source DF Seg ss 1 10282.9 birthrat docs 1 320.0 Normal Probabity Plotof the Residals Hgramhe Residls

College Algebra
College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...
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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 docs + 3.15 Inphones + 0.0219 urban 112 cases used, 10 cases contain missing values SE Coef 2.613 Predictor Coef T P VIF Constant 71.668 27.43 0.00o birthrat -0.32548 0.06032 -5.40 0.000 3.624 0.06900 0.4674 docs -0.00002 3.1495 3.484 Inphones urban 6.74 0.000 4.783 0.02189 0.02692 0.81 0.418 2.534 s - 4.37467 R-Sq - 85.3% R-Sq (adj) - 84.7% Analysis of Variance p-value 0.000 Source DF s MS Regression 4 11843.9 2047.7 13891.7 Residual Error 107 19.1 Total 111 Seq ss 10282.9 Source DF birthrat 1 docs 1 320.0 Normel Probability Plot of the Residuals (resporse isee) Histogramof tthe Residuals mpore ee 10 15 Residual
Transcribed Image Text: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 docs + 3.15 Inphones + 0.0219 urban 112 cases used, 10 cases contain missing values SE Coef 2.613 Predictor Coef T P VIF Constant 71.668 27.43 0.00o birthrat -0.32548 0.06032 -5.40 0.000 3.624 0.06900 0.4674 docs -0.00002 3.1495 3.484 Inphones urban 6.74 0.000 4.783 0.02189 0.02692 0.81 0.418 2.534 s - 4.37467 R-Sq - 85.3% R-Sq (adj) - 84.7% Analysis of Variance p-value 0.000 Source DF s MS Regression 4 11843.9 2047.7 13891.7 Residual Error 107 19.1 Total 111 Seq ss 10282.9 Source DF birthrat 1 docs 1 320.0 Normel Probability Plot of the Residuals (resporse isee) Histogramof tthe Residuals mpore ee 10 15 Residual
Residuals Versus the Fitted Values (response is lifeexp) 10- 5- -5- 10- -15- 50 55 60 65 75 80 70 Fitted Value 85 Inphones urban 1 1228.4 1 12.7 Unusual Observations Obs birthrat 46.0 lifeexpf 47.000 41.000 56.000 75.000 Fit SE Fit Residual -9.456 St Resid -2.19R 14 56.456 0.670 -12.301 -8.945 9.870 17 42.0 53.301 0.854 -2.87R -2.08R 2.27R 19 28.0 64.945 65.130 0.766 49 33.0 0.439 2.14R 2.31R -2.34R 62 33.0 74.000 64.666 0.430 9.334 80 29.0 67.000 57.332 1.247 9.668 81 44.0 43.000 53.053 0.789 -10.053 -6.714 8.660 83 40.0 48.000 54.714 2.192 -1.77 X 88 21.0 74.000 65.340 1.238 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 95% 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 docs 75.0 50.0 Inphones 3.00 urban 1 76.0 Scatterplot of lifeexpf vs birthrat, docs, Inphones, urban birthrat docs • 80 70 -60 50 40 20 60 0 10 20 30 40 Inphones 40 urban 80 70 60- 50 40 2 25 50 75 100
Transcribed Image Text:Residuals Versus the Fitted Values (response is lifeexp) 10- 5- -5- 10- -15- 50 55 60 65 75 80 70 Fitted Value 85 Inphones urban 1 1228.4 1 12.7 Unusual Observations Obs birthrat 46.0 lifeexpf 47.000 41.000 56.000 75.000 Fit SE Fit Residual -9.456 St Resid -2.19R 14 56.456 0.670 -12.301 -8.945 9.870 17 42.0 53.301 0.854 -2.87R -2.08R 2.27R 19 28.0 64.945 65.130 0.766 49 33.0 0.439 2.14R 2.31R -2.34R 62 33.0 74.000 64.666 0.430 9.334 80 29.0 67.000 57.332 1.247 9.668 81 44.0 43.000 53.053 0.789 -10.053 -6.714 8.660 83 40.0 48.000 54.714 2.192 -1.77 X 88 21.0 74.000 65.340 1.238 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 95% 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 docs 75.0 50.0 Inphones 3.00 urban 1 76.0 Scatterplot of lifeexpf vs birthrat, docs, Inphones, urban birthrat docs • 80 70 -60 50 40 20 60 0 10 20 30 40 Inphones 40 urban 80 70 60- 50 40 2 25 50 75 100
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