Example 8.1: Log Wage Equation with Heteroscedasticity-Robust Standard Errors
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage2
gen single=(~married)
gen male=(~female)
gen marrmale=male*married
gen marrfem=female*married
gen singfem=single*female
reg lwage marrmale marrfem singfem educ exper expersq tenure tenursq, robust
Regression with robust standard errors Number of obs = 526
F( 8, 517) = 51.70
Prob > F = 0.0000
R-squared = 0.4609
Root MSE = .39329
------------------------------------------------------------------------------
| Robust
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
marrmale | .2126756 .0571419 3.72 0.000 .1004167 .3249345
marrfem | -.1982676 .05877 -3.37 0.001 -.313725 -.0828102
singfem | -.1103502 .0571163 -1.93 0.054 -.2225587 .0018583
educ | .0789103 .0074147 10.64 0.000 .0643437 .0934769
exper | .0268006 .0051391 5.22 0.000 .0167044 .0368967
expersq | -.0005352 .0001063 -5.03 0.000 -.0007442 -.0003263
tenure | .0290875 .0069409 4.19 0.000 .0154516 .0427234
tenursq | -.0005331 .0002437 -2.19 0.029 -.0010119 -.0000544
_cons | .321378 .109469 2.94 0.003 .1063193 .5364368
------------------------------------------------------------------------------
reg lwage marrmale marrfem singfem educ exper expersq tenure tenursq
Source | SS df MS Number of obs = 526
-------------+------------------------------ F( 8, 517) = 55.25
Model | 68.3617614 8 8.54522017 Prob > F = 0.0000
Residual | 79.9680004 517 .154676983 R-squared = 0.4609
-------------+------------------------------ Adj R-squared = 0.4525
Total | 148.329762 525 .28253288 Root MSE = .39329
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
marrmale | .2126756 .0553572 3.84 0.000 .103923 .3214283
marrfem | -.1982676 .0578355 -3.43 0.001 -.3118891 -.0846462
singfem | -.1103502 .0557421 -1.98 0.048 -.219859 -.0008414
educ | .0789103 .0066945 11.79 0.000 .0657585 .0920621
exper | .0268006 .0052428 5.11 0.000 .0165007 .0371005
expersq | -.0005352 .0001104 -4.85 0.000 -.0007522 -.0003183
tenure | .0290875 .006762 4.30 0.000 .0158031 .0423719
tenursq | -.0005331 .0002312 -2.31 0.022 -.0009874 -.0000789
_cons | .321378 .100009 3.21 0.001 .1249041 .517852
------------------------------------------------------------------------------
Example 8.2: Heteroscedastisity-Robust F Statistics
use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa3
reg cumgpa sat hsperc tothrs female black white if term==2, robust
Regression with robust standard errors Number of obs = 366
F( 6, 359) = 39.30
Prob > F = 0.0000
R-squared = 0.4006
Root MSE = .46929
------------------------------------------------------------------------------
| Robust
cumgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sat | .0011407 .0001915 5.96 0.000 .0007641 .0015174
hsperc | -.0085664 .0014179 -6.04 0.000 -.0113548 -.0057779
tothrs | .002504 .0007406 3.38 0.001 .0010475 .0039605
female | .3034333 .0591378 5.13 0.000 .1871332 .4197334
black | -.1282837 .1192413 -1.08 0.283 -.3627829 .1062155
white | -.0587217 .111392 -0.53 0.598 -.2777846 .1603411
_cons | 1.470065 .2206802 6.66 0.000 1.036076 1.904053
------------------------------------------------------------------------------
reg cumgpa sat hsperc tothrs female black white if term==2
Source | SS df MS Number of obs = 366
-------------+------------------------------ F( 6, 359) = 39.98
Model | 52.831358 6 8.80522634 Prob > F = 0.0000
Residual | 79.062328 359 .220229326 R-squared = 0.4006
-------------+------------------------------ Adj R-squared = 0.3905
Total | 131.893686 365 .361352564 Root MSE = .46929
------------------------------------------------------------------------------
cumgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sat | .0011407 .0001786 6.39 0.000 .0007896 .0014919
hsperc | -.0085664 .0012404 -6.91 0.000 -.0110058 -.006127
tothrs | .002504 .000731 3.43 0.001 .0010664 .0039415
female | .3034333 .0590203 5.14 0.000 .1873643 .4195023
black | -.1282837 .1473701 -0.87 0.385 -.4181009 .1615335
white | -.0587217 .1409896 -0.42 0.677 -.3359909 .2185475
_cons | 1.470065 .2298031 6.40 0.000 1.018135 1.921994
------------------------------------------------------------------------------
Example 8.3: Heteroskedasticity-Robust LM Statistic
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime1
gen avgsensq=avgsen*avgsen
reg narr86 pcnv avgsen avgsensq ptime86 qemp86 inc86 black hispan, robust
Regression with robust standard errors Number of obs = 2725
F( 8, 2716) = 29.84
Prob > F = 0.0000
R-squared = 0.0728
Root MSE = .82843
------------------------------------------------------------------------------
| Robust
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.1355954 .0336218 -4.03 0.000 -.2015223 -.0696685
avgsen | .0178411 .0101233 1.76 0.078 -.0020091 .0376913
avgsensq | -.0005163 .0002077 -2.49 0.013 -.0009236 -.0001091
ptime86 | -.03936 .0062236 -6.32 0.000 -.0515634 -.0271566
qemp86 | -.0505072 .0142015 -3.56 0.000 -.078354 -.0226603
inc86 | -.0014797 .0002295 -6.45 0.000 -.0019297 -.0010296
black | .3246024 .0585135 5.55 0.000 .2098669 .439338
hispan | .19338 .0402983 4.80 0.000 .1143616 .2723985
_cons | .5670128 .0402756 14.08 0.000 .4880389 .6459867
------------------------------------------------------------------------------
Turning point for avgsen
di _b[avgsen]/(2*_b[avgsensq])
-17.276862
reg narr86 pcnv ptime86 qemp86 inc86 black hispan
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 6, 2718) = 34.95
Model | 143.977563 6 23.9962606 Prob > F = 0.0000
Residual | 1866.36959 2718 .686670196 R-squared = 0.0716
-------------+------------------------------ Adj R-squared = 0.0696
Total | 2010.34716 2724 .738012906 Root MSE = .82866
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.1322784 .0403406 -3.28 0.001 -.2113797 -.0531771
ptime86 | -.0377953 .008497 -4.45 0.000 -.0544566 -.021134
qemp86 | -.0509814 .0144359 -3.53 0.000 -.0792878 -.022675
inc86 | -.00149 .0003404 -4.38 0.000 -.0021575 -.0008224
black | .3296885 .0451778 7.30 0.000 .2411022 .4182748
hispan | .1954509 .0396929 4.92 0.000 .1176195 .2732823
_cons | .5703344 .0360073 15.84 0.000 .49973 .6409388
------------------------------------------------------------------------------
predict ubar1, resid
qui reg avgsen pcnv ptime86 qemp86 inc86 black hispan
predict r1, r
qui reg avgsensq pcnv ptime86 qemp86 inc86 black hispan
predict r2, r
qui gen ur1 = ubar1*r1
qui gen ur2 = ubar1*r2
gen iota = 1
reg iota ur1 ur2, noconstant
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 2, 2723) = 2.00
Model | 3.99708536 2 1.99854268 Prob > F = 0.1355
Residual | 2721.00291 2723 .999266586 R-squared = 0.0015
-------------+------------------------------ Adj R-squared = 0.0007
Total | 2725.00 2725 1.00 Root MSE = .99963
------------------------------------------------------------------------------
iota | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ur1 | .0277846 .0140598 1.98 0.048 .0002156 .0553537
ur2 | -.0010447 .0005479 -1.91 0.057 -.002119 .0000296
------------------------------------------------------------------------------
scalar hetlm = e(N)-e(rss)
scalar pval = chi2tail(2,hetlm)
display _n "Robust LM statistic : " %6.3f hetlm /*
> */ _n "Under H0, distrib Chi2(2), p-value: " %5.3f pval
Robust LM statistic : 3.997
Under H0, distrib Chi2(2), p-value: 0.136
reg narr86 pcnv ptime86 qemp86 inc86 black hispan
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 6, 2718) = 34.95
Model | 143.977563 6 23.9962606 Prob > F = 0.0000
Residual | 1866.36959 2718 .686670196 R-squared = 0.0716
-------------+------------------------------ Adj R-squared = 0.0696
Total | 2010.34716 2724 .738012906 Root MSE = .82866
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.1322784 .0403406 -3.28 0.001 -.2113797 -.0531771
ptime86 | -.0377953 .008497 -4.45 0.000 -.0544566 -.021134
qemp86 | -.0509814 .0144359 -3.53 0.000 -.0792878 -.022675
inc86 | -.00149 .0003404 -4.38 0.000 -.0021575 -.0008224
black | .3296885 .0451778 7.30 0.000 .2411022 .4182748
hispan | .1954509 .0396929 4.92 0.000 .1176195 .2732823
_cons | .5703344 .0360073 15.84 0.000 .49973 .6409388
-----------------------------------------------------------------------------
predict ubar2, resid
reg ubar2 pcnv avgsen avgsensq ptime86 qemp86 inc86 black hispan
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 8, 2716) = 0.43
Model | 2.37155739 8 .296444674 Prob > F = 0.9025
Residual | 1863.99804 2716 .686302664 R-squared = 0.0013
-------------+------------------------------ Adj R-squared = -0.0017
Total | 1866.36959 2724 .685157707 Root MSE = .82843
------------------------------------------------------------------------------
ubar1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.003317 .0403699 -0.08 0.935 -.0824758 .0758418
avgsen | .0178411 .009696 1.84 0.066 -.0011713 .0368534
avgsensq | -.0005163 .000297 -1.74 0.082 -.0010987 .0000661
ptime86 | -.0015647 .0086935 -0.18 0.857 -.0186112 .0154819
qemp86 | .0004742 .0144345 0.03 0.974 -.0278295 .0287779
inc86 | .0000103 .0003405 0.03 0.976 -.0006574 .000678
black | -.0050861 .0454188 -0.11 0.911 -.094145 .0839729
hispan | -.0020709 .0397035 -0.05 0.958 -.0799229 .0757812
_cons | -.0033216 .0360573 -0.09 0.927 -.0740242 .0673809
------------------------------------------------------------------------------
scalar lm1 = e(N)*e(r2)
display _n "LM statistic : " %6.3f lm1 /*
LM statistic : 3.5425
Example 8.4: Heteroscedasticity in Housing Price Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/hprice1
reg price lotsize sqrft bdrms
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 3, 84) = 57.46
Model | 617130.701 3 205710.234 Prob > F = 0.0000
Residual | 300723.805 84 3580.0453 R-squared = 0.6724
-------------+------------------------------ Adj R-squared = 0.6607
Total | 917854.506 87 10550.0518 Root MSE = 59.833
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lotsize | .0020677 .0006421 3.22 0.002 .0007908 .0033446
sqrft | .1227782 .0132374 9.28 0.000 .0964541 .1491022
bdrms | 13.85252 9.010145 1.54 0.128 -4.06514 31.77018
_cons | -21.77031 29.47504 -0.74 0.462 -80.38466 36.84404
------------------------------------------------------------------------------
whitetst, fitted
White's special test statistic : 16.26842 Chi-sq( 2) P-value = 2.9e-04
reg lprice llotsize lsqrft bdrms
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 3, 84) = 50.42
Model | 5.15504028 3 1.71834676 Prob > F = 0.0000
Residual | 2.86256324 84 .034078134 R-squared = 0.6430
-------------+------------------------------ Adj R-squared = 0.6302
Total | 8.01760352 87 .092156362 Root MSE = .1846
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | .1679667 .0382812 4.39 0.000 .0918404 .244093
lsqrft | .7002324 .0928652 7.54 0.000 .5155597 .8849051
bdrms | .0369584 .0275313 1.34 0.183 -.0177906 .0917074
_cons | -1.297042 .6512836 -1.99 0.050 -2.592191 -.0018931
------------------------------------------------------------------------------
whitetst, fitted
White's special test statistic : 3.447243 Chi-sq( 2) P-value = .1784
Example 8.5: Special Form of the White Test in the Log Housing Price Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/hprice1
reg lprice llotsize lsqrft bdrms
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 3, 84) = 50.42
Model | 5.15506425 3 1.71835475 Prob > F = 0.0000
Residual | 2.86255771 84 .034078068 R-squared = 0.6430
-------------+------------------------------ Adj R-squared = 0.6302
Total | 8.01762195 87 .092156574 Root MSE = .1846
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | .167968 .0382811 4.39 0.000 .0918418 .2440941
lsqrft | .7002326 .0928652 7.54 0.000 .5155601 .8849051
bdrms | .0369585 .0275313 1.34 0.183 -.0177905 .0917075
_cons | 5.6107 .6512829 8.61 0.000 4.315553 6.905848
------------------------------------------------------------------------------
whitetst, fitted
White's special test statistic : 3.447286 Chi-sq( 2) P-value = .1784
Example 8.6: Family Saving Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/saving
reg sav inc
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 1, 98) = 6.49
Model | 66368437.0 1 66368437.0 Prob > F = 0.0124
Residual | 1.0019e+09 98 10223460.8 R-squared = 0.0621
-------------+------------------------------ Adj R-squared = 0.0526
Total | 1.0683e+09 99 10790581.8 Root MSE = 3197.4
------------------------------------------------------------------------------
sav | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inc | .1466283 .0575488 2.55 0.012 .0324247 .260832
_cons | 124.8424 655.3931 0.19 0.849 -1175.764 1425.449
------------------------------------------------------------------------------
reg sav inc [aw = 1/inc]
(sum of wgt is 1.3877e-02)
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 1, 98) = 9.14
Model | 58142339.8 1 58142339.8 Prob > F = 0.0032
Residual | 623432468 98 6361555.80 R-squared = 0.0853
-------------+------------------------------ Adj R-squared = 0.0760
Total | 681574808 99 6884594.02 Root MSE = 2522.2
------------------------------------------------------------------------------
sav | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inc | .1717555 .0568128 3.02 0.003 .0590124 .2844986
_cons | -124.9528 480.8606 -0.26 0.796 -1079.205 829.2994
------------------------------------------------------------------------------
reg sav inc size educ age black
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 5, 94) = 1.70
Model | 88426246.4 5 17685249.3 Prob > F = 0.1430
Residual | 979841351 94 10423844.2 R-squared = 0.0828
-------------+------------------------------ Adj R-squared = 0.0340
Total | 1.0683e+09 99 10790581.8 Root MSE = 3228.6
------------------------------------------------------------------------------
sav | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inc | .109455 .0714317 1.53 0.129 -.0323742 .2512842
size | 67.66119 222.9642 0.30 0.762 -375.0395 510.3619
educ | 151.8235 117.2487 1.29 0.199 -80.97646 384.6235
age | .2857217 50.03108 0.01 0.995 -99.05217 99.62361
black | 518.3934 1308.063 0.40 0.693 -2078.796 3115.583
_cons | -1605.416 2830.707 -0.57 0.572 -7225.851 4015.019
------------------------------------------------------------------------------
reg sav inc size educ age black [aw = 1/inc]
(sum of wgt is 1.3877e-02)
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 5, 94) = 2.19
Model | 71020334.9 5 14204067.0 Prob > F = 0.0621
Residual | 610554473 94 6495260.35 R-squared = 0.1042
-------------+------------------------------ Adj R-squared = 0.0566
Total | 681574808 99 6884594.02 Root MSE = 2548.6
------------------------------------------------------------------------------
sav | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inc | .1005179 .0772511 1.30 0.196 -.052866 .2539017
size | -6.868501 168.4327 -0.04 0.968 -341.2956 327.5586
educ | 139.4802 100.5362 1.39 0.169 -60.1368 339.0972
age | 21.74721 41.30598 0.53 0.600 -60.26678 103.7612
black | 137.2842 844.5941 0.16 0.871 -1539.677 1814.246
_cons | -1854.814 2351.797 -0.79 0.432 -6524.362 2814.734
------------------------------------------------------------------------------
Example 8.7: Demand for Cigarettes
use http://fmwww.bc.edu/ec-p/data/wooldridge/smoke
reg cigs lincome lcigpric educ age agesq restaurn
Source | SS df MS Number of obs = 807
-------------+------------------------------ F( 6, 800) = 7.42
Model | 8003.02506 6 1333.83751 Prob > F = 0.0000
Residual | 143750.658 800 179.688322 R-squared = 0.0527
-------------+------------------------------ Adj R-squared = 0.0456
Total | 151753.683 806 188.280003 Root MSE = 13.405
------------------------------------------------------------------------------
cigs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lincome | .8802689 .7277838 1.21 0.227 -.5483223 2.30886
lcigpric | -.7508498 5.773343 -0.13 0.897 -12.08354 10.58184
educ | -.5014982 .1670772 -3.00 0.003 -.8294597 -.1735368
age | .7706936 .1601223 4.81 0.000 .456384 1.085003
agesq | -.0090228 .001743 -5.18 0.000 -.0124443 -.0056013
restaurn | -2.825085 1.111794 -2.54 0.011 -5.007462 -.642708
_cons | -3.639884 24.07866 -0.15 0.880 -50.9047 43.62493
------------------------------------------------------------------------------
Change in cigs if income increases by 10%
display _b[lincome]*10/100
.08802689
Turnover point for age
display _b[age]/2/_b[agesq]
-42.708116
whitetst, fitted
White's special test statistic : 26.57258 Chi-sq( 2) P-value = 1.7e-06
gen lubar=log(ub*ub)
qui reg lubar lincome lcigpric educ age agesq restaurn
predict cigsh, xb
gen cigse = exp(cigsh)
reg cigs lincome lcigpric educ age agesq restaurn [aw=1/cigse]
(sum of wgt is 1.9977e+01)
Source | SS df MS Number of obs = 807
-------------+------------------------------ F( 6, 800) = 17.06
Model | 10302.6415 6 1717.10692 Prob > F = 0.0000
Residual | 80542.0684 800 100.677586 R-squared = 0.1134
-------------+------------------------------ Adj R-squared = 0.1068
Total | 90844.71 806 112.710558 Root MSE = 10.034
------------------------------------------------------------------------------
cigs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lincome | 1.295241 .4370118 2.96 0.003 .4374154 2.153066
lcigpric | -2.94028 4.460142 -0.66 0.510 -11.69524 5.814684
educ | -.4634462 .1201586 -3.86 0.000 -.6993095 -.2275829
age | .4819474 .0968082 4.98 0.000 .2919194 .6719755
agesq | -.0056272 .0009395 -5.99 0.000 -.0074713 -.0037831
restaurn | -3.461066 .7955047 -4.35 0.000 -5.022589 -1.899543
_cons | 5.63533 17.80313 0.32 0.752 -29.31103 40.58169
------------------------------------------------------------------------------
Example 8.8: Labor Force Participation of Married Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz
reg inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Source | SS df MS Number of obs = 753
-------------+------------------------------ F( 7, 745) = 38.22
Model | 48.8080578 7 6.97257968 Prob > F = 0.0000
Residual | 135.919698 745 .182442547 R-squared = 0.2642
-------------+------------------------------ Adj R-squared = 0.2573
Total | 184.727756 752 .245648611 Root MSE = .42713
------------------------------------------------------------------------------
inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0034052 .0014485 -2.35 0.019 -.0062488 -.0005616
educ | .0379953 .007376 5.15 0.000 .023515 .0524756
exper | .0394924 .0056727 6.96 0.000 .0283561 .0506287
expersq | -.0005963 .0001848 -3.23 0.001 -.0009591 -.0002335
age | -.0160908 .0024847 -6.48 0.000 -.0209686 -.011213
kidslt6 | -.2618105 .0335058 -7.81 0.000 -.3275875 -.1960335
kidsge6 | .0130122 .013196 0.99 0.324 -.0128935 .0389179
_cons | .5855192 .154178 3.80 0.000 .2828442 .8881943
------------------------------------------------------------------------------
reg inlf nwifeinc educ exper expersq age kidslt6 kidsge6, robust
Regression with robust standard errors Number of obs = 753
F( 7, 745) = 62.48
Prob > F = 0.0000
R-squared = 0.2642
Root MSE = .42713
------------------------------------------------------------------------------
| Robust
inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0034052 .0015249 -2.23 0.026 -.0063988 -.0004115
educ | .0379953 .007266 5.23 0.000 .023731 .0522596
exper | .0394924 .00581 6.80 0.000 .0280864 .0508983
expersq | -.0005963 .00019 -3.14 0.002 -.0009693 -.0002233
age | -.0160908 .002399 -6.71 0.000 -.0208004 -.0113812
kidslt6 | -.2618105 .0317832 -8.24 0.000 -.3242058 -.1994152
kidsge6 | .0130122 .0135329 0.96 0.337 -.013555 .0395795
_cons | .5855192 .1522599 3.85 0.000 .2866098 .8844287
------------------------------------------------------------------------------
Example 8.9: Determinants of Personal Computer Ownership
use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1
gen parcoll = (mothcoll | fathcoll)
reg PC hsGPA ACT parcoll
Source | SS df MS Number of obs = 141
-------------+------------------------------ F( 3, 137) = 1.98
Model | 1.40186813 3 .467289377 Prob > F = 0.1201
Residual | 32.3569971 137 .236182461 R-squared = 0.0415
-------------+------------------------------ Adj R-squared = 0.0205
Total | 33.7588652 140 .241134752 Root MSE = .48599
------------------------------------------------------------------------------
PC | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hsGPA | .0653943 .1372576 0.48 0.635 -.2060231 .3368118
ACT | .0005645 .0154967 0.04 0.971 -.0300792 .0312082
parcoll | .2210541 .092957 2.38 0.019 .037238 .4048702
_cons | -.0004322 .4905358 -0.00 0.999 -.970433 .9695686
------------------------------------------------------------------------------
predict phat
gen h=phat*(1-phat)
reg PC hsGPA ACT parcoll [aw=1/h]
(sum of wgt is 6.2818e+02)
Source | SS df MS Number of obs = 141
-------------+------------------------------ F( 3, 137) = 2.22
Model | 1.54663033 3 .515543445 Prob > F = 0.0882
Residual | 31.7573194 137 .231805251 R-squared = 0.0464
-------------+------------------------------ Adj R-squared = 0.0256
Total | 33.3039497 140 .237885355 Root MSE = .48146
------------------------------------------------------------------------------
PC | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hsGPA | .0327029 .1298817 0.25 0.802 -.2241292 .289535
ACT | .004272 .0154527 0.28 0.783 -.0262847 .0348286
parcoll | .2151862 .0862918 2.49 0.014 .04455 .3858224
_cons | .0262099 .4766498 0.05 0.956 -.9163323 .9687521
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This page prepared by Oleksandr Talavera (revised 8 Nov 2002)
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