You have data on a sample of women of childbearing age in Botswana, which you want to use to study what affects the decision of having children. For each woman in the sample, you observe the binary variable child for whether they have children, years of education (educ), age in years (age), an indicator for living in a urban area (urban), an indicator for presence of electricity in the house (electric), and an indicator for ever being married (evermarr). You run the following two Probit regressions in Stata: • probit child c.age##c.age educ i.urban i.electric i.evermarr, nolog Probit regression Number of ebs 4.358
You have data on a sample of women of childbearing age in Botswana, which you want to use to study what affects the decision of having children. For each woman in the sample, you observe the binary variable child for whether they have children, years of education (educ), age in years (age), an indicator for living in a urban area (urban), an indicator for presence of electricity in the house (electric), and an indicator for ever being married (evermarr). You run the following two Probit regressions in Stata: • probit child c.age##c.age educ i.urban i.electric i.evermarr, nolog Probit regression Number of ebs 4.358
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
You have data on a sample of women of childbearing age in Botswana, which you want to use to
study what affects the decision of having children. For each woman in the sample, you observe
the binary variable child for whether they have children, years of education (educ), age in years
(age), an indicator for living in a urban area (urban), an indicator for presence of electricity in
the house (electric), and an indicator for ever being married (evermarr). You run the following
two Probit regressions in Stata:
• probit child c.age##c.age educ i.urban i.electric i.evermarr, nolog
Probit regression
Number of obs
4,358
LR chi2(6)
1981.05
Prob > chi2
Pseudo R2
0.0000
Log likelihood = -1505.7243
0.3968
child |
Coef.
Std. Err.
P>|z|
(95% Conf. Interval]
age |
.498523
.021328
23.37
0.000
.4567208
.5403252
c.age#c.age |
-.0068284
.0003493
-19.55
0.000
-.0075129
-.0061438
educ I
-.0157116
.0076358
-2.06
0.040
-.0306775
-.0007457
-.16576
-.3189878
.0453264
-.0118135
1.urban |
-.0602168
.0538495
-1.12
0.263
1.electric |
-.1654007
.0783622
-2.11
0.035
1.evermarr |
.3586488
.0626046
5.73
0.000
.2359461
.4813515
_cons |
-7.106355
.2972466
-23.91
0.000
-7.688948
-6.523763
• probit child educ i.urban i.electric i.evermarr, nolog
Probit regression
Number of obs
4,358
LR chi2(4)
841.60
Prob
chi2
0.0000
Log likelihood =
-2075.45
Pseudo R2
0.1686
child I
Coef.
Std. Err.
P>|z|
[95% Conf. Interval]
educ |
-.0496809
.0063896
-7.78
0.000
-.0622044
.0371574
1. urban |
1.electric I
-.0670348
.0468442
-1.43
0.152
-.1588479
.0247782
.0341736
.0682229
0.50
0.616
-.0995409
.1678881
1. evermarr |
1.196584
.0498832
23.99
0.000
1.098815
1.294353
cons|
.5463433
.051708
10.57
0.000
.4449974
.6476891
The Likelihood Ratio Test statistic to test the null hypothesis that age has no effect on the
probability of having children is
O LR = -1505.7243 - 2075.45 = -3581.1743
O LR = 2 x (-1505.7243 + 2075.45) = 1139.4514
O LR = -1505.7243 + 2075.45 = 569.7257
O LR = 2 x (-1505.7243 - 2075.45) =-7162.3486](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F210a42b5-deb4-472f-8443-bf5a12e90db6%2F3c7b7722-6b1e-4940-a5bc-3ebb077d8ffb%2F7eun3j_processed.png&w=3840&q=75)
Transcribed Image Text:QUESTION 4
You have data on a sample of women of childbearing age in Botswana, which you want to use to
study what affects the decision of having children. For each woman in the sample, you observe
the binary variable child for whether they have children, years of education (educ), age in years
(age), an indicator for living in a urban area (urban), an indicator for presence of electricity in
the house (electric), and an indicator for ever being married (evermarr). You run the following
two Probit regressions in Stata:
• probit child c.age##c.age educ i.urban i.electric i.evermarr, nolog
Probit regression
Number of obs
4,358
LR chi2(6)
1981.05
Prob > chi2
Pseudo R2
0.0000
Log likelihood = -1505.7243
0.3968
child |
Coef.
Std. Err.
P>|z|
(95% Conf. Interval]
age |
.498523
.021328
23.37
0.000
.4567208
.5403252
c.age#c.age |
-.0068284
.0003493
-19.55
0.000
-.0075129
-.0061438
educ I
-.0157116
.0076358
-2.06
0.040
-.0306775
-.0007457
-.16576
-.3189878
.0453264
-.0118135
1.urban |
-.0602168
.0538495
-1.12
0.263
1.electric |
-.1654007
.0783622
-2.11
0.035
1.evermarr |
.3586488
.0626046
5.73
0.000
.2359461
.4813515
_cons |
-7.106355
.2972466
-23.91
0.000
-7.688948
-6.523763
• probit child educ i.urban i.electric i.evermarr, nolog
Probit regression
Number of obs
4,358
LR chi2(4)
841.60
Prob
chi2
0.0000
Log likelihood =
-2075.45
Pseudo R2
0.1686
child I
Coef.
Std. Err.
P>|z|
[95% Conf. Interval]
educ |
-.0496809
.0063896
-7.78
0.000
-.0622044
.0371574
1. urban |
1.electric I
-.0670348
.0468442
-1.43
0.152
-.1588479
.0247782
.0341736
.0682229
0.50
0.616
-.0995409
.1678881
1. evermarr |
1.196584
.0498832
23.99
0.000
1.098815
1.294353
cons|
.5463433
.051708
10.57
0.000
.4449974
.6476891
The Likelihood Ratio Test statistic to test the null hypothesis that age has no effect on the
probability of having children is
O LR = -1505.7243 - 2075.45 = -3581.1743
O LR = 2 x (-1505.7243 + 2075.45) = 1139.4514
O LR = -1505.7243 + 2075.45 = 569.7257
O LR = 2 x (-1505.7243 - 2075.45) =-7162.3486
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