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

------------------------------------------------------------------------------

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

------------------------------------------------------------------------------

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.