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.
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
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F8acc7dc3-2265-4174-a01d-a23e0b8a230a%2F6c1ef71d-e7e6-4a5e-b51d-5821e60ace92%2F38sglbq_processed.png&w=3840&q=75)
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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