a) Interpret the impact of docs on lifeexpf. b) Test whether the model can be used for predicting female life expectancy in a country [use a 95% confidence level] and show your work. c) Does the graph below [a plot of the residuals against fitted values] seem to contradict the answer to part b)? Please explain why or why not.

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Question 4.
including 122 countries by regressing "lifeexpf" onto “docs". The variables included are:
The following regression results are drawn from a UN data set
lifeexpf: female life expectancy in years [i.e., the average for all females in each country]
docs: number of doctors per 10,000 of the population.
Regression Analysis: lifeexpf versus docs
The regression equation is
lifeexpf - 58.2 + 0.783 docs
Predictor
Coef SE Coef
T
P
58.2470
0.78324
0.000
Constant
docs
0.8811
66.10
0.05772
13.57 0.000
S =
R-Sq = 60.7%
R-Sq (adj) = 60.4%
Analysis of Variance
Source
DF
s
MS
F
P
184.14
Regression
Residual Error
1
9082.7
9082.7
119
5869.5
Total
120
14952.2
Please answer the following questions:
a) Interpret the impact of docs on lifeexpf.
b) Test whether the model can be used for predicting female life expectancy in a country
[use a 95% confidence level] and show your work.
c) Does the graph below [a plot of the residuals against fitted values] seem to contradict
the answer to part b)? Please explain why or why not.
Residuals Versus the Fitted Values
(response is lifeexpf)
20-
10-
-10-
-20-
60
65
70
75
80
85
90
95
Fitted Value
Residual
Transcribed Image Text:Question 4. including 122 countries by regressing "lifeexpf" onto “docs". The variables included are: The following regression results are drawn from a UN data set lifeexpf: female life expectancy in years [i.e., the average for all females in each country] docs: number of doctors per 10,000 of the population. Regression Analysis: lifeexpf versus docs The regression equation is lifeexpf - 58.2 + 0.783 docs Predictor Coef SE Coef T P 58.2470 0.78324 0.000 Constant docs 0.8811 66.10 0.05772 13.57 0.000 S = R-Sq = 60.7% R-Sq (adj) = 60.4% Analysis of Variance Source DF s MS F P 184.14 Regression Residual Error 1 9082.7 9082.7 119 5869.5 Total 120 14952.2 Please answer the following questions: a) Interpret the impact of docs on lifeexpf. b) Test whether the model can be used for predicting female life expectancy in a country [use a 95% confidence level] and show your work. c) Does the graph below [a plot of the residuals against fitted values] seem to contradict the answer to part b)? Please explain why or why not. Residuals Versus the Fitted Values (response is lifeexpf) 20- 10- -10- -20- 60 65 70 75 80 85 90 95 Fitted Value Residual
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