empirical exercises 6
TRANSCRIPT
6-1
a) reg course_eval beauty
Source | SS df MS Number of obs = 463
-------------+------------------------------ F( 1, 461) = 17.08
Model | 5.08300731 1 5.08300731 Prob > F = 0.0000
Residual | 137.155613 461 .297517598 R-squared = 0.0357
-------------+------------------------------ Adj R-squared = 0.0336
Total | 142.23862 462 .307875801 Root MSE = .54545
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course_eval | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
beauty | .1330014 .0321775 4.13 0.000 .0697687 .1962342
_cons | 3.998272 .0253493 157.73 0.000 3.948458 4.048087
The estimated slope of the regression is 0.1330014
b) reg course_eval beauty intro onecredit female minority nnenglish
Source | SS df MS Number of obs = 463
-------------+------------------------------ F( 6, 456) = 13.90
Model | 21.9971655 6 3.66619426 Prob > F = 0.0000
Residual | 120.241455 456 .263687401 R-squared = 0.1546
-------------+------------------------------ Adj R-squared = 0.1435
Total | 142.23862 462 .307875801 Root MSE = .51351
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course_eval | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
beauty | .16561 .0307296 5.39 0.000 .1052208 .2259991
intro | .011325 .0544778 0.21 0.835 -.0957338 .1183838
onecredit | .6345271 .1113391 5.70 0.000 .4157257 .8533284
female | -.1734774 .0492791 -3.52 0.000 -.2703197 -.0766352
minority | -.1666154 .0762784 -2.18 0.029 -.3165162 -.0167147
nnenglish | -.2441613 .1069578 -2.28 0.023 -.4543527 -.0339699
_cons | 4.068289 .037543 108.36 0.000 3.99451 4.142068
For each additional unit of Beauty, Course Evaluation will be increased by 0.166. The R2 of the regression in (a) is too slow. It is 0.0357; it shows us that there is no correlation between the variables, so there must be some omitted variable bias that can be relevant in order to make the regression acceptable.
c) Professor Smith Course EvaluationCourse Evaluation = 4.07 + 0.166Beaty+ 0.011Intro + 0.634OneCredit
-0.173Female – 0.167Minority – 0.244NNNEnglish
Course Evaluation = 4.07 + 0.166(4.75) + 0.011(0) + 0.634(0) – 0.173(0) –0.167(1) -0.244(0)
Course Evaluation = 4.6915
6-2
A.. reg ed dist
Source | SS df MS Number of obs = 3796
-------------+------------------------------ F( 1, 3794) = 28.48
Model | 93.0256754 1 93.0256754 Prob > F = 0.0000
Residual | 12394.3568 3794 3.266831 R-squared = 0.0074
-------------+------------------------------ Adj R-squared = 0.0072
Total | 12487.3825 3795 3.29048287 Root MSE = 1.8074
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ed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist | -.0733727 .0137498 -5.34 0.000 -.1003304 -.046415
_cons | 13.95586 .0377241 369.95 0.000 13.88189 14.02982
Estimated slope is -0.0733727
B. . reg ed dist bytest female black hispanic incomehi ownhome dadcoll cue80 stwmfg80
Source | SS df MS Number of obs = 3796
-------------+------------------------------ F( 10, 3785) = 146.35
Model | 3481.95254 10 348.195254 Prob > F = 0.0000
Residual | 9005.42997 3785 2.37924173 R-squared = 0.2788
-------------+------------------------------ Adj R-squared = 0.2769
Total | 12487.3825 3795 3.29048287 Root MSE = 1.5425
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ed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist | -.0315387 .0123703 -2.55 0.011 -.0557918 -.0072857
bytest | .0938201 .0031622 29.67 0.000 .0876204 .1000199
female | .145408 .0505889 2.87 0.004 .0462239 .244592
black | .367971 .071363 5.16 0.000 .2280574 .5078846
hispanic | .3985196 .0744617 5.35 0.000 .2525308 .5445085
incomehi | .3951984 .0605308 6.53 0.000 .2765222 .5138746
ownhome | .1521313 .0668075 2.28 0.023 .0211492 .2831135
dadcoll | .6961324 .0687248 10.13 0.000 .5613911 .8308737
cue80 | .0232052 .0096321 2.41 0.016 .0043207 .0420898
stwmfg80 | -.0517777 .0198523 -2.61 0.009 -.0906999 -.0128556
_cons | 8.827518 .2502782 35.27 0.000 8.336825 9.318211
The effect on ED from Dist is -0.0315387.
C. Yes it is substantially different, taking into consideration that the difference is 0.041834 in slopes. No, it does not suffer from omitted variable bias.
D. R Squared for A is 0.0074 and R Squared for B is 0.2788 ; STR for A is 0.0137498 and B 0.0123703; Adjusted RSquare for A is 0.0072 and for B is 0.2788. Since Adjusted R Squared is being taken from a population rather than a sample, Adjusted R Squared is of little or no different value from R Squared.
E. This coefficient stated that if the student’s father is a College Graduate the odds of completing education become higher.
F. They appear because employment would have been easier to achieve if education was completed, therefore the higher the unemployment rate, the higher the motivation to complete education. State hourly wage in manufacturing appears because the higher the wage, the more students would be likely to drop out of school to go earn money quick. Yes the signs are what I believed because it represents the most logical scenario. For every unit of unemployment rate that goes up, completed education will go up by 0.232052 and for every unit of state hourly earnings in manufacturing completed education will go down by 0.517777.
G. Y= 8.83 - 0.032(2) + 0.094(58) + 0.395 + 0.152 + 0.0232(7.5) – 0.5178(9.75) = 9.89 years of ED
H. Y= 8.83 - 0.032(4) + 0.094(58) + 0.395 + 0.152 + 0.0232(7.5) – 0.5178(9.75) = 9.83 years of ED
6 - 3
a. summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
growth | 64 1.86912 1.816189 -2.811944 7.156855
rgdp60 | 64 3130.813 2522.979 366.9999 9895.004
tradeshare | 64 .5423919 .2283326 .140502 1.127937
yearsschool | 64 3.959219 2.553465 .2 10.07
rev_coups | 64 .1700666 .2254557 0 .9703704
-------------+--------------------------------------------------------
assasinati~s | 64 .281901 .494159 0 2.466667
b.
Source | SS df MS Number of obs = 64
-------------+------------------------------ F( 5, 58) = 4.76
Model | 60.4973376 5 12.0994675 Prob > F = 0.0010
Residual | 147.310822 58 2.53984176 R-squared = 0.2911
-------------+------------------------------ Adj R-squared = 0.2300
Total | 207.80816 63 3.29854222 Root MSE = 1.5937
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growth | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tradeshare | 1.340819 .9600631 1.40 0.168 -.5809558 3.262594
yearsschool | .5642445 .1431131 3.94 0.000 .2777726 .8507165
rev_coups | -2.150426 1.11859 -1.92 0.059 -4.389527 .0886756
assasinati~s | .3225844 .4880043 0.66 0.511 -.6542624 1.299431
rgdp60 | -.0004613 .0001508 -3.06 0.003 -.0007631 -.0001594
_cons | .6268915 .783028 0.80 0.427 -.9405093 2.194292
The value of the coefficient on Rev_Coups is -2.15 and it is relatively large in a real world-sense
because not all the countries went through revolutions, insurrections or coup d’etats during the
period of 1960 to 1995.
c. Growth= 0.6268915+ 1.340819(0.5423919)+0.5642445 (3.959219)-2.150426 (0.1700666)+0.3225844(0.281901)-0.0004613 (3130.813)= 1.8690856
d. Growth= 0.6268915+ 1.340819(0.5423919+ 0.2283326)+0.5642445 (3.959219)-2.150426 (0.1700666)+0.3225844(0.281901)-0.0004613 (3130.813)= 2.175367314
e. Oil variable is omitted from the regression because even though the oil will explain a significant part of a country growth rate, not all the countries have this rich natural resource.