Example 3.1: Determinants of College GPA
use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1
summ ACT
Variable | Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
ACT | 141 24.15603 2.844252 16 33
reg colGPA hsGPA ACT
Source | SS df MS Number of obs = 141
---------+------------------------------ F( 2, 138) = 14.78
Model | 3.42365506 2 1.71182753 Prob > F = 0.0000
Residual | 15.9824444 138 .115814814 R-squared = 0.1764
---------+------------------------------ Adj R-squared = 0.1645
Total | 19.4060994 140 .138614996 Root MSE = .34032
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
hsGPA | .4534559 .0958129 4.733 0.000 .2640047 .6429071
ACT | .009426 .0107772 0.875 0.383 -.0118838 .0307358
_cons | 1.286328 .3408221 3.774 0.000 .612419 1.960237
------------------------------------------------------------------------------
reg colGPA ACT
Source | SS df MS Number of obs = 141
---------+------------------------------ F( 1, 139) = 6.21
Model | .829558811 1 .829558811 Prob > F = 0.0139
Residual | 18.5765406 139 .133644177 R-squared = 0.0427
---------+------------------------------ Adj R-squared = 0.0359
Total | 19.4060994 140 .138614996 Root MSE = .36557
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
ACT | .027064 .0108628 2.491 0.014 .0055862 .0485417
_cons | 2.402979 .2642027 9.095 0.000 1.880604 2.925355
------------------------------------------------------------------------------
Example 3.2: Hourly Wage Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
reg lwage educ exper tenure
Source | SS df MS Number of obs = 526
---------+------------------------------ F( 3, 522) = 80.39
Model | 46.8741805 3 15.6247268 Prob > F = 0.0000
Residual | 101.455581 522 .194359351 R-squared = 0.3160
---------+------------------------------ Adj R-squared = 0.3121
Total | 148.329762 525 .28253288 Root MSE = .44086
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
educ | .092029 .0073299 12.555 0.000 .0776292 .1064288
exper | .0041211 .0017233 2.391 0.017 .0007357 .0075065
tenure | .0220672 .0030936 7.133 0.000 .0159897 .0281448
_cons | .2843595 .1041904 2.729 0.007 .0796755 .4890435
------------------------------------------------------------------------------
Example 3.3: Participation in 401(K) Pension Plan
use http://fmwww.bc.edu/ec-p/data/wooldridge/401k
summ prate mrate age
Variable | Obs Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
prate | 1534 87.36291 16.71654 3 100
mrate | 1534 .7315124 .7795393 .01 4.91
age | 1534 13.18123 9.171114 4 51
reg prate mrate age
Source | SS df MS Number of obs = 1534
-------------+------------------------------ F( 2, 1531) = 77.79
Model | 39517.1118 2 19758.5559 Prob > F = 0.0000
Residual | 388868.428 1531 253.99636 R-squared = 0.0922
-------------+------------------------------ Adj R-squared = 0.0911
Total | 428385.539 1533 279.442622 Root MSE = 15.937
------------------------------------------------------------------------------
prate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mrate | 5.521289 .5258844 10.50 0.000 4.489759 6.552819
age | .2431466 .0446999 5.44 0.000 .1554671 .330826
_cons | 80.11905 .7790208 102.85 0.000 78.59099 81.64711
------------------------------------------------------------------------------
reg prate mrate
Source | SS df MS Number of obs = 1534
-------------+------------------------------ F( 1, 1532) = 123.68
Model | 32001.7271 1 32001.7271 Prob > F = 0.0000
Residual | 396383.812 1532 258.73617 R-squared = 0.0747
-------------+------------------------------ Adj R-squared = 0.0741
Total | 428385.539 1533 279.442622 Root MSE = 16.085
------------------------------------------------------------------------------
prate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mrate | 5.861079 .5270107 11.12 0.000 4.82734 6.894818
_cons | 83.07546 .5632844 147.48 0.000 81.97057 84.18035
------------------------------------------------------------------------------
Example 3.4: Determinants of College GPA
use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1
reg colGPA hsGPA ACT
Source | SS df MS Number of obs = 141
---------+------------------------------ F( 2, 138) = 14.78
Model | 3.42365506 2 1.71182753 Prob > F = 0.0000
Residual | 15.9824444 138 .115814814 R-squared = 0.1764
---------+------------------------------ Adj R-squared = 0.1645
Total | 19.4060994 140 .138614996 Root MSE = .34032
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
hsGPA | .4534559 .0958129 4.733 0.000 .2640047 .6429071
ACT | .009426 .0107772 0.875 0.383 -.0118838 .0307358
_cons | 1.286328 .3408221 3.774 0.000 .612419 1.960237
------------------------------------------------------------------------------
Example 3.5: Explaining Arrest Records
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime1
sum narr86 pcnv avgsen ptime86 qemp86
Variable | Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
narr86 | 2725 .4044037 .8590768 0 12
pcnv | 2725 .3577872 .395192 0 1
avgsen | 2725 .6322936 3.508031 0 59.2
ptime86 | 2725 .387156 1.950051 0 12
qemp86 | 2725 2.309028 1.610428 0 4
reg narr86 pcnv ptime86 qemp86
Source | SS df MS Number of obs = 2725
---------+------------------------------ F( 3, 2721) = 39.10
Model | 83.0741941 3 27.691398 Prob > F = 0.0000
Residual | 1927.27296 2721 .708295833 R-squared = 0.0413
---------+------------------------------ Adj R-squared = 0.0403
Total | 2010.34716 2724 .738012906 Root MSE = .8416
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
pcnv | -.1499274 .0408653 -3.669 0.000 -.2300576 -.0697973
ptime86 | -.0344199 .008591 -4.007 0.000 -.0512655 -.0175744
qemp86 | -.104113 .0103877 -10.023 0.000 -.1244816 -.0837445
_cons | .7117715 .0330066 21.565 0.000 .647051 .776492
------------------------------------------------------------------------------
Change in the predicted number of arrests when proportion of convictions increases by .5 for 1 man
display _b[pcnv]*.5
-.075
Change in the predicted number of arrests when proportion of convictions increases by .5 for 100 men
display 100*_b[pcnv]*.5
-7.5
Change in the predicted number of arrests when prison term increases by 12
display _b[ptime86]*12
-.408
Change in the predicted number of arrests when legal employment increases by a quarter for 100 men
display _b[qemp86]*100
-10.4
reg narr86 pcnv avgsen ptime86 qemp86
Source | SS df MS Number of obs = 2725
---------+------------------------------ F( 4, 2720) = 29.96
Model | 84.8242895 4 21.2060724 Prob > F = 0.0000
Residual | 1925.52287 2720 .707912819 R-squared = 0.0422
---------+------------------------------ Adj R-squared = 0.0408
Total | 2010.34716 2724 .738012906 Root MSE = .84138
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
pcnv | -.1508319 .0408583 -3.692 0.000 -.2309484 -.0707154
avgsen | .0074431 .0047338 1.572 0.116 -.0018392 .0167254
ptime86 | -.0373908 .0087941 -4.252 0.000 -.0546345 -.0201471
qemp86 | -.103341 .0103965 -9.940 0.000 -.1237268 -.0829552
_cons | .7067565 .0331515 21.319 0.000 .6417519 .771761
------------------------------------------------------------------------------
Example 3.6: Hourly Wage Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
reg lwage educ
Source | SS df MS Number of obs = 526
---------+------------------------------ F( 1, 524) = 119.58
Model | 27.5606296 1 27.5606296 Prob > F = 0.0000
Residual | 120.769132 524 .230475443 R-squared = 0.1858
---------+------------------------------ Adj R-squared = 0.1843
Total | 148.329762 525 .28253288 Root MSE = .48008
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
educ | .0827444 .0075667 10.935 0.000 .0678796 .0976092
_cons | .5837726 .0973358 5.998 0.000 .3925562 .774989
------------------------------------------------------------------------------
This page prepared by Oleksandr Talavera (revised 13 Sep 2002)
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
These pages are maintained by the Faculty Micro
Resource Center's GSA Program,
a unit of Boston College Academic Technology Services