Part D. Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are used to estimate MODEL 1: The variables used in the analysis are: infmort is number of deaths within the year per 1,000 live births 1pcinc is natural log of per capita income 1popul is natural log of the population (the population is in thousands) Iphysic is natural log of physicians per 100,000 inhabitants d90 is year dummy for 1990. For questions 1 to 4 you can assume that MLR 1-4 are satisfied. 1. Use the Stata output below to interpret 3. Test at a 5% significance level whether the number of physicians per capita has any effect on infant mortality rate. . reg infmort 1pcinc 1popul 1physic d90 Source Model Residual Total infmort SS infmort = Bo + ß₁lpcinc + ß₂lpopul + ß3lphysic+d₁d90 + u 78.0499129 350.452136 428.502049 df 4 19.5124782 97 3.61290862 MS 101 4.24259454 Coef. Std. Err. 1pcinc -4.693354 1.638132 1popul -.0551426 .1885884 1physic 3.954843 .9733102 -.090968 .4754863 34.82137 12.55572 d90 _cons Number of obs F(4, 97) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE -2.87 0.005 -0.29 0.771 4.06 0.000 -0.19 0.849 2.77 0.007 = = -7.944593 -.4294383 2.023092 -1.034677 9.901751 = 102 5.40 0.0006 0.1821 0.1484 1.9008 [95% Conf. Interval] -1.442115 .3191531 5.886595 .8527407 59.741
Part D. Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are used to estimate MODEL 1: The variables used in the analysis are: infmort is number of deaths within the year per 1,000 live births 1pcinc is natural log of per capita income 1popul is natural log of the population (the population is in thousands) Iphysic is natural log of physicians per 100,000 inhabitants d90 is year dummy for 1990. For questions 1 to 4 you can assume that MLR 1-4 are satisfied. 1. Use the Stata output below to interpret 3. Test at a 5% significance level whether the number of physicians per capita has any effect on infant mortality rate. . reg infmort 1pcinc 1popul 1physic d90 Source Model Residual Total infmort SS infmort = Bo + ß₁lpcinc + ß₂lpopul + ß3lphysic+d₁d90 + u 78.0499129 350.452136 428.502049 df 4 19.5124782 97 3.61290862 MS 101 4.24259454 Coef. Std. Err. 1pcinc -4.693354 1.638132 1popul -.0551426 .1885884 1physic 3.954843 .9733102 -.090968 .4754863 34.82137 12.55572 d90 _cons Number of obs F(4, 97) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE -2.87 0.005 -0.29 0.771 4.06 0.000 -0.19 0.849 2.77 0.007 = = -7.944593 -.4294383 2.023092 -1.034677 9.901751 = 102 5.40 0.0006 0.1821 0.1484 1.9008 [95% Conf. Interval] -1.442115 .3191531 5.886595 .8527407 59.741
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Could you answer subquestions 4 and 5?
![Part D.
Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are
used to estimate MODEL 1:
The variables used in the analysis are:
infmort is number of deaths within the year per 1,000 live births
Ipcinc is natural log of per capita income
Ipopul is natural log of the population (the population is in thousands)
Iphysic is natural log of physicians per 100,000 inhabitants
d90 is year dummy for 1990.
For questions 1 to 4 you can assume that MLR 1-4 are satisfied.
A
1. Use the Stata output below to interpret ß3. Test at a 5% significance level whether the number of physicians per capita has any effect on
infant mortality rate.
reg infmort 1pcinc 1popul 1physic d90
Source
Model
Residual
Total
infmort
infmort = Po + B₁lpcinc + B₂lpopul + ß3lphysic + 8₁ d90 + u
SS
78.0499129
350.452136
428.502049
df
.
19.5124782
4
97 3.61290862
Coef. Std. Err.
MS
101 4.24259454
1pcinc -4.693354 1.638132
1popul
- .0551426
1885884
1physic
3.954843
.9733102
d90
-.090968 .4754863
34.82137 12.55572
_cons
Number of obs
F(4, 97)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
-2.87 0.005
-0.29 0.771
4.06 0.000
-0.19 0.849
2.77
0.007
=
-7.944593
-.4294383
2.023092
-1.034677
9.901751
=
102
5.40
0.0006
0.1821
0.1484
1.9008
[95% Conf. Interval]
-1.442115
.3191531
5.886595
.8527407
59.741](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbc7b432a-c535-47af-8100-952bfa513a2d%2Fd33c135a-e32f-450c-be03-b0de0864df4b%2Fu8jaada_processed.png&w=3840&q=75)
Transcribed Image Text:Part D.
Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are
used to estimate MODEL 1:
The variables used in the analysis are:
infmort is number of deaths within the year per 1,000 live births
Ipcinc is natural log of per capita income
Ipopul is natural log of the population (the population is in thousands)
Iphysic is natural log of physicians per 100,000 inhabitants
d90 is year dummy for 1990.
For questions 1 to 4 you can assume that MLR 1-4 are satisfied.
A
1. Use the Stata output below to interpret ß3. Test at a 5% significance level whether the number of physicians per capita has any effect on
infant mortality rate.
reg infmort 1pcinc 1popul 1physic d90
Source
Model
Residual
Total
infmort
infmort = Po + B₁lpcinc + B₂lpopul + ß3lphysic + 8₁ d90 + u
SS
78.0499129
350.452136
428.502049
df
.
19.5124782
4
97 3.61290862
Coef. Std. Err.
MS
101 4.24259454
1pcinc -4.693354 1.638132
1popul
- .0551426
1885884
1physic
3.954843
.9733102
d90
-.090968 .4754863
34.82137 12.55572
_cons
Number of obs
F(4, 97)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
-2.87 0.005
-0.29 0.771
4.06 0.000
-0.19 0.849
2.77
0.007
=
-7.944593
-.4294383
2.023092
-1.034677
9.901751
=
102
5.40
0.0006
0.1821
0.1484
1.9008
[95% Conf. Interval]
-1.442115
.3191531
5.886595
.8527407
59.741
![2. The model excludes a dummy variable for year 1987. Why has the year dummy for 1987 been excluded? How do you interpret the
regression result for the coefficient of the year dummy for 1990 using the Stata output in question 1?
3. Using the Stata output in question 1 and the below Stata output to answer this question. Can we reject at the 5% significance level the
null hypothesis that neither per capita income nor population has effect on infant mortality rate (₁ = 0 and ß₂ = 0).
reg infmort 1physic d90
Source
Model
Residual
Total
infmort
1physic
d90
_cons
SS
47.5816152
380.920434
428.502049
df
2
99
101
Coef. Std. Err.
1.928748 .7001339
-.9190813 .3892766
.0220238 3.680659
MS
23.7908076
3.84768115
4.24259454
Number of obs
F(2, 99)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
2.75 0.007
-2.36 0.020
0.01 0.995
=
.5395308
-1.691491
-7.281203
=
=
=
=
102
6.18
0.0029
0.1110
0.0931
1.9616
[95% Conf. Interval]
3.317966
.1466721
7.32525
4. 25 states introduced a free pre-pregnancy and prenatal care program in 1988, while the rest did not have such a program. Without
controlling for any other factors, propose a method that allows you to investigate the effect of the free pre-pregnancy and prenatal care
program on infant mortality rate. Interpret coefficients in your model.
5. Suppose that states in the South tend to have more infant mortality cases because they have more days of extreme heat. At the same
time states in the South tend to be poorer. Explain why this would generate biased estimates in MODEL 1 and propose how to modify this
model. Notice that you do not observe if a state is located in the South.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbc7b432a-c535-47af-8100-952bfa513a2d%2Fd33c135a-e32f-450c-be03-b0de0864df4b%2Fhjn2smp_processed.png&w=3840&q=75)
Transcribed Image Text:2. The model excludes a dummy variable for year 1987. Why has the year dummy for 1987 been excluded? How do you interpret the
regression result for the coefficient of the year dummy for 1990 using the Stata output in question 1?
3. Using the Stata output in question 1 and the below Stata output to answer this question. Can we reject at the 5% significance level the
null hypothesis that neither per capita income nor population has effect on infant mortality rate (₁ = 0 and ß₂ = 0).
reg infmort 1physic d90
Source
Model
Residual
Total
infmort
1physic
d90
_cons
SS
47.5816152
380.920434
428.502049
df
2
99
101
Coef. Std. Err.
1.928748 .7001339
-.9190813 .3892766
.0220238 3.680659
MS
23.7908076
3.84768115
4.24259454
Number of obs
F(2, 99)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
2.75 0.007
-2.36 0.020
0.01 0.995
=
.5395308
-1.691491
-7.281203
=
=
=
=
102
6.18
0.0029
0.1110
0.0931
1.9616
[95% Conf. Interval]
3.317966
.1466721
7.32525
4. 25 states introduced a free pre-pregnancy and prenatal care program in 1988, while the rest did not have such a program. Without
controlling for any other factors, propose a method that allows you to investigate the effect of the free pre-pregnancy and prenatal care
program on infant mortality rate. Interpret coefficients in your model.
5. Suppose that states in the South tend to have more infant mortality cases because they have more days of extreme heat. At the same
time states in the South tend to be poorer. Explain why this would generate biased estimates in MODEL 1 and propose how to modify this
model. Notice that you do not observe if a state is located in the South.
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