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February 24, 2005
HW, due 3/1/05
Use Monte Carlo simulations to verify the major points in the chapters to this point.
The log file shows you how to do things manually.
The do file is probably the most efficient way to run repeated simulations.
We can illustrate the implications of these equations:
Posted by bparke at 11:03 AM | Comments (0)
February 22, 2005
Left-Out / Irrelevant Variables
We considered models with one and two regressors, where the issue is the possible inclusion of X3.
Here, the C's are sample covariances, the V's are sample covariances, and the r's are correlations.
If X3 is included, the formula for the estimate of b2 differs from the formula if X3 is not included. The estimate of b2 depends on the covariance between X2 and X3 and the covariance between X3 and Y.
Left-Out Variable: X3 is left-out when it should not be. The formula for b2 is wrong, producing bias. The only exception is when the sample covariance between X2 and X3 is zero.
Irrelevant Variable: X3 is put in when it is not needed. The formula for b2 does not produce bias because it is not wrong to estimate b3, which is zero. The formula for the variance of b2 shows that the estimate loses efficiency because the sample correlation between X2 and X3 increases the variance of b2 (unless that sample correlation is zero).
Multicollinearity: the degree of linear association among the regressors. In this case, the degree of collinearity is the covariance/correlation between X2 and X3. The left-out variable and irrelevant variable problems are more important when this collinearity is strong.
The example we discussed was a regression of income on education with the ability variable playing the role of X3. If ability is not available, then you can only consider the possible effects of the left-out variable bias as the X3 term is forced to be part of the error term.
Posted by bparke at 08:34 PM | Comments (0)
February 15, 2005
Functional Form
Tues./Thurs.
Posted by bparke at 08:47 PM | Comments (0)
Hypothesis Testing Review
Posted by bparke at 12:02 PM | Comments (0)
February 10, 2005
F Tests
Posted by bparke at 10:54 PM | Comments (0)
Basketball Data - III
(I am working at getting the spacing right before I add the pictures and text.). log close log: T:\170\class3.log log type: text closed on: 10 Feb 2005, 12:24:21 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ log: T:\170\class3.log log type: text opened on: 10 Feb 2005, 11:49:49 . regress diff at diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 2, 13) = 11.82 Model | 781.717037 2 390.858518 Prob > F = 0.0012 Residual | 430.032963 13 33.0794587 R-squared = 0.6451 -------------+------------------------------ Adj R-squared = 0.5905 Total | 1211.75 15 80.7833333 Root MSE = 5.7515 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -9.663939 2.927789 -3.30 0.006 -15.98904 -3.338835 diff1 | .5075395 .1758791 2.89 0.013 .1275758 .8875031 _cons | 5.514436 2.322087 2.37 0.034 .4978725 10.531 ------------------------------------------------------------------------------ . regress diff at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 10.05 Model | 506.25 1 506.25 Prob > F = 0.0068 Residual | 705.5 14 50.3928571 R-squared = 0.4178 -------------+------------------------------ Adj R-squared = 0.3762 Total | 1211.75 15 80.7833333 Root MSE = 7.0988 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -11.25 3.549396 -3.17 0.007 -18.8627 -3.637302 _cons | 8.75 2.509802 3.49 0.004 3.36701 14.13299 ------------------------------------------------------------------------------ . regress diff Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 0, 15) = 0.00 Model | 0 0 . Prob > F = . Residual | 1211.75 15 80.7833333 R-squared = 0.0000 -------------+------------------------------ Adj R-squared = 0.0000 Total | 1211.75 15 80.7833333 Root MSE = 8.988 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 3.125 2.246989 1.39 0.185 -1.664343 7.914343 ------------------------------------------------------------------------------ . regress diff at diff1 nc1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 3, 12) = 8.15 Model | 812.972324 3 270.990775 Prob > F = 0.0032 Residual | 398.777676 12 33.231473 R-squared = 0.6709 -------------+------------------------------ Adj R-squared = 0.5886 Total | 1211.75 15 80.7833333 Root MSE = 5.7647 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -9.956003 2.949921 -3.38 0.006 -16.38333 -3.528677 diff1 | .6272934 .2152286 2.91 0.013 .1583505 1.096236 nc1 | -.2132144 .2198516 -0.97 0.351 -.6922298 .265801 _cons | 13.73266 8.787856 1.56 0.144 -5.414432 32.87975 ------------------------------------------------------------------------------ . regress diff at nc1 opp1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 3, 12) = 8.15 Model | 812.972324 3 270.990775 Prob > F = 0.0032 Residual | 398.777676 12 33.231473 R-squared = 0.6709 -------------+------------------------------ Adj R-squared = 0.5886 Total | 1211.75 15 80.7833333 Root MSE = 5.7647 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -9.956003 2.949921 -3.38 0.006 -16.38333 -3.528677 nc1 | .414079 .2009048 2.06 0.062 -.0236549 .851813 opp1 | -.6272934 .2152286 -2.91 0.013 -1.096236 -.1583505 _cons | 13.73266 8.787856 1.56 0.144 -5.414432 32.87975 ------------------------------------------------------------------------------
Posted by bparke at 10:37 PM | Comments (0)
February 08, 2005
Interpreting Regressions
Posted by bparke at 10:45 PM | Comments (0)
Basketball Data - II
------------------------------------------------------------------------------------------------------ log: T:\170\class2.log log type: text opened on: 8 Feb 2005, 11:10:15 . regress diff2 diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 2.89 Model | 163.002541 1 163.002541 Prob > F = 0.1114 Residual | 790.434959 14 56.4596399 R-squared = 0.1710 -------------+------------------------------ Adj R-squared = 0.1117 Total | 953.4375 15 63.5625 Root MSE = 7.514 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | -.383479 .2256906 -1.70 0.111 -.8675372 .1005792 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . summarize diff2 diff1 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- diff2 | 16 -1.6875 7.972609 -14 17 diff1 | 16 4.8125 8.596269 -9 20 . tabulate win1 at, summarize(win) Means, Standard Deviations and Frequencies of win | at win1 | 0 1 | Total -----------+----------------------+---------- 0 | .33333333 0 | .2 | .57735027 0 | .4472136 | 3 2 | 5 -----------+----------------------+---------- 1 | 1 .33333333 | .63636364 | 0 .51639778 | .50452498 | 5 6 | 11 -----------+----------------------+---------- Total | .75 .25 | .5 | .46291005 .46291005 | .51639778 | 8 8 | 16 . tabulate win1 at, summarize(win) nomeans nostandard Frequencies of win | at win1 | 0 1 | Total -----------+----------------------+---------- 0 | 3 2 | 5 1 | 5 6 | 11 -----------+----------------------+---------- Total | 8 8 | 16 . tabulate win1 at, summarize(win) nostandard Means and Frequencies of win | at win1 | 0 1 | Total -----------+----------------------+---------- 0 | .33333333 0 | .2 | 3 2 | 5 -----------+----------------------+---------- 1 | 1 .33333333 | .63636364 | 5 6 | 11 -----------+----------------------+---------- Total | .75 .25 | .5 | 8 8 | 16 . - preserve - sort at - restore . tabulate diff at, summarize(win) nostandard Means and Frequencies of win | at diff | 0 1 | Total -----------+----------------------+---------- -11 | . 0 | 0 | 0 1 | 1 -----------+----------------------+---------- -9 | . 0 | 0 | 0 1 | 1 -----------+----------------------+---------- -6 | . 0 | 0 | 0 1 | 1 -----------+----------------------+---------- -5 | . 0 | 0 | 0 1 | 1 -----------+----------------------+---------- -2 | . 0 | 0 | 0 1 | 1 -----------+----------------------+---------- 0 | 0 0 | 0 | 2 1 | 3 -----------+----------------------+---------- 2 | 1 . | 1 | 1 0 | 1 -----------+----------------------+---------- 6 | . 1 | 1 | 0 1 | 1 -----------+----------------------+---------- 7 | 1 1 | 1 | 1 1 | 2 -----------+----------------------+---------- 11 | 1 . | 1 | 1 0 | 1 -----------+----------------------+---------- 15 | 1 . | 1 | 1 0 | 1 -----------+----------------------+---------- 16 | 1 . | 1 | 1 0 | 1 -----------+----------------------+---------- 19 | 1 . | 1 | 1 0 | 1 -----------+----------------------+---------- Total | .75 .25 | .5 | 8 8 | 16 . tabulate win1 at, summarize(diff) nostandard Means and Frequencies of diff | at win1 | 0 1 | Total -----------+----------------------+---------- 0 | .66666667 -10 | -3.6 | 3 2 | 5 -----------+----------------------+---------- 1 | 13.6 0 | 6.1818182 | 5 6 | 11 -----------+----------------------+---------- Total | 8.75 -2.5 | 3.125 | 8 8 | 16 . regress diff at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 10.05 Model | 506.25 1 506.25 Prob > F = 0.0068 Residual | 705.5 14 50.3928571 R-squared = 0.4178 -------------+------------------------------ Adj R-squared = 0.3762 Total | 1211.75 15 80.7833333 Root MSE = 7.0988 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -11.25 3.549396 -3.17 0.007 -18.8627 -3.637302 _cons | 8.75 2.509802 3.49 0.004 3.36701 14.13299 ------------------------------------------------------------------------------ . regress diff win1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 5.22 Model | 328.913636 1 328.913636 Prob > F = 0.0385 Residual | 882.836364 14 63.0597403 R-squared = 0.2714 -------------+------------------------------ Adj R-squared = 0.2194 Total | 1211.75 15 80.7833333 Root MSE = 7.941 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- win1 | 9.781818 4.283066 2.28 0.039 .5955559 18.96808 _cons | -3.6 3.55133 -1.01 0.328 -11.21685 4.016846 ------------------------------------------------------------------------------ . regress diff win1 at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 2, 13) = 25.13 Model | 962.712963 2 481.356481 Prob > F = 0.0000 Residual | 249.037037 13 19.1566952 R-squared = 0.7945 -------------+------------------------------ Adj R-squared = 0.7629 Total | 1211.75 15 80.7833333 Root MSE = 4.3768 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- win1 | 11.62963 2.382448 4.88 0.000 6.482664 16.7766 at | -12.7037 2.208588 -5.75 0.000 -17.47507 -7.932339 _cons | 1.481481 2.147509 0.69 0.502 -3.157931 6.120894 ------------------------------------------------------------------------------ . regress diff2 diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 2.89 Model | 163.002541 1 163.002541 Prob > F = 0.1114 Residual | 790.434959 14 56.4596399 R-squared = 0.1710 -------------+------------------------------ Adj R-squared = 0.1117 Total | 953.4375 15 63.5625 Root MSE = 7.514 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | -.383479 .2256906 -1.70 0.111 -.8675372 .1005792 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . regress diff diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 7.46 Model | 421.315041 1 421.315041 Prob > F = 0.0162 Residual | 790.434959 14 56.4596399 R-squared = 0.3477 -------------+------------------------------ Adj R-squared = 0.3011 Total | 1211.75 15 80.7833333 Root MSE = 7.514 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | .616521 .2256906 2.73 0.016 .1324628 1.100579 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . regress diff2 diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 2.89 Model | 163.002541 1 163.002541 Prob > F = 0.1114 Residual | 790.434959 14 56.4596399 R-squared = 0.1710 -------------+------------------------------ Adj R-squared = 0.1117 Total | 953.4375 15 63.5625 Root MSE = 7.514 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | -.383479 .2256906 -1.70 0.111 -.8675372 .1005792 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . regress diff2 diff1 at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 2, 13) = 7.91 Model | 523.404537 2 261.702268 Prob > F = 0.0057 Residual | 430.032963 13 33.0794587 R-squared = 0.5490 -------------+------------------------------ Adj R-squared = 0.4796 Total | 953.4375 15 63.5625 Root MSE = 5.7515 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | -.4924605 .1758791 -2.80 0.015 -.8724242 -.1124969 at | -9.663939 2.927789 -3.30 0.006 -15.98904 -3.338835 _cons | 5.514436 2.322087 2.37 0.034 .4978725 10.531 ------------------------------------------------------------------------------ . regress diff2 nc1 opp1 at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 3, 12) = 5.56 Model | 554.659824 3 184.886608 Prob > F = 0.0126 Residual | 398.777676 12 33.231473 R-squared = 0.5817 -------------+------------------------------ Adj R-squared = 0.4772 Total | 953.4375 15 63.5625 Root MSE = 5.7647 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nc1 | -.585921 .2009048 -2.92 0.013 -1.023655 -.148187 opp1 | .3727066 .2152286 1.73 0.109 -.0962363 .8416495 at | -9.956003 2.949921 -3.38 0.006 -16.38333 -3.528677 _cons | 13.73266 8.787856 1.56 0.144 -5.414432 32.87975 ------------------------------------------------------------------------------ . exit, clear
Posted by bparke at 07:38 PM | Comments (0)
February 03, 2005
Regression Assumptions
Posted by bparke at 10:17 PM | Comments (0)
Basketball Data - I
-------------------------------------------------------------------------------------------------------- log: T:\170\class1.log log type: text opened on: 3 Feb 2005, 11:40:28 . summarize Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- n | 16 8.5 4.760952 1 16 game | 0 at | 16 .5 .5163978 0 1 nc1 | 16 40.5625 8.421946 26 55 nc2 | 16 38.875 7.228416 29 54 -------------+-------------------------------------------------------- opp1 | 16 35.75 7.715785 18 47 opp2 | 16 40.5625 6.791846 28 54 nc | 16 79.4375 11.26037 65 103 opp | 16 76.3125 10.35193 53 90 diff | 16 3.125 8.987955 -11 19 -------------+-------------------------------------------------------- diff1 | 16 4.8125 8.596269 -9 20 diff2 | 16 -1.6875 7.972609 -14 17 win | 16 .5 .5163978 0 1 win1 | 16 .6875 .4787136 0 1 win2 | 16 .375 .5 0 1 . twoway (scatter opp nc) . regress opp nc Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 10.63 Model | 693.90827 1 693.90827 Prob > F = 0.0057 Residual | 913.52923 14 65.2520879 R-squared = 0.4317 -------------+------------------------------ Adj R-squared = 0.3911 Total | 1607.4375 15 107.1625 Root MSE = 8.0779 ------------------------------------------------------------------------------ opp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nc | .6040222 .1852248 3.26 0.006 .2067546 1.00129 _cons | 28.33049 14.85173 1.91 0.077 -3.523314 60.18428 ------------------------------------------------------------------------------ . regress nc opp Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 10.63 Model | 821.039798 1 821.039798 Prob > F = 0.0057 Residual | 1080.8977 14 77.2069787 R-squared = 0.4317 -------------+------------------------------ Adj R-squared = 0.3911 Total | 1901.9375 15 126.795833 Root MSE = 8.7868 ------------------------------------------------------------------------------ nc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- opp | .7146856 .21916 3.26 0.006 .2446343 1.184737 _cons | 24.89805 16.86829 1.48 0.162 -11.28083 61.07693 ------------------------------------------------------------------------------ . correlate no observations r(2000); . - preserve . correlate nc nc1 nc2 opp opp1 opp2 (obs=16) | nc nc1 nc2 opp opp1 opp2 -------------+------------------------------------------------------ nc | 1.0000 nc1 | 0.7670 1.0000 nc2 | 0.6642 0.0297 1.0000 opp | 0.6570 0.6058 0.3177 1.0000 opp1 | 0.3988 0.4352 0.1142 0.7547 1.0000 opp2 | 0.5484 0.4288 0.3546 0.6668 0.0143 1.0000 . regress nc2 nc1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 0.01 Model | .691549668 1 .691549668 Prob > F = 0.9130 Residual | 783.05845 14 55.9327465 R-squared = 0.0009 -------------+------------------------------ Adj R-squared = -0.0705 Total | 783.75 15 52.25 Root MSE = 7.4788 ------------------------------------------------------------------------------ nc2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nc1 | .0254949 .2292847 0.11 0.913 -.4662718 .5172616 _cons | 37.84086 9.486437 3.99 0.001 17.49448 58.18725 ------------------------------------------------------------------------------ . regress opp2 opp1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 0.00 Model | .141727324 1 .141727324 Prob > F = 0.9580 Residual | 691.795773 14 49.4139838 R-squared = 0.0002 -------------+------------------------------ Adj R-squared = -0.0712 Total | 691.9375 15 46.1291667 Root MSE = 7.0295 ------------------------------------------------------------------------------ opp2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- opp1 | .012598 .2352335 0.05 0.958 -.4919277 .5171237 _cons | 40.11212 8.591258 4.67 0.000 21.68571 58.53854 ------------------------------------------------------------------------------ . twoway (scatter nc2 nc1) . twoway (scatter opp2 opp1) . twoway (scatter nc2 nc1) . regress nc nc1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 20.00 Model | 1118.87905 1 1118.87905 Prob > F = 0.0005 Residual | 783.05845 14 55.9327465 R-squared = 0.5883 -------------+------------------------------ Adj R-squared = 0.5589 Total | 1901.9375 15 126.795833 Root MSE = 7.4788 ------------------------------------------------------------------------------ nc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nc1 | 1.025495 .2292847 4.47 0.001 .5337282 1.517262 _cons | 37.84086 9.486437 3.99 0.001 17.49448 58.18725 ------------------------------------------------------------------------------ . regress opp opp1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 18.53 Model | 915.641727 1 915.641727 Prob > F = 0.0007 Residual | 691.795773 14 49.4139838 R-squared = 0.5696 -------------+------------------------------ Adj R-squared = 0.5389 Total | 1607.4375 15 107.1625 Root MSE = 7.0295 ------------------------------------------------------------------------------ opp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- opp1 | 1.012598 .2352335 4.30 0.001 .5080723 1.517124 _cons | 40.11212 8.591258 4.67 0.000 21.68571 58.53854 ------------------------------------------------------------------------------ . twoway (scatter nc1 nc) . twoway (scatter nc nc1) . twoway (scatter opp opp1) . regress diff2 diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 2.89 Model | 163.002541 1 163.002541 Prob > F = 0.1114 Residual | 790.434959 14 56.4596399 R-squared = 0.1710 -------------+------------------------------ Adj R-squared = 0.1117 Total | 953.4375 15 63.5625 Root MSE = 7.514 ------------------------------------------------------------------------------ diff2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | -.383479 .2256906 -1.70 0.111 -.8675372 .1005792 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . regress diff diff1 Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 7.46 Model | 421.315041 1 421.315041 Prob > F = 0.0162 Residual | 790.434959 14 56.4596399 R-squared = 0.3477 -------------+------------------------------ Adj R-squared = 0.3011 Total | 1211.75 15 80.7833333 Root MSE = 7.514 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- diff1 | .616521 .2256906 2.73 0.016 .1324628 1.100579 _cons | .1579927 2.169889 0.07 0.943 -4.495957 4.811942 ------------------------------------------------------------------------------ . twoway (scatter diff diff1) . regress diff at Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 1, 14) = 10.05 Model | 506.25 1 506.25 Prob > F = 0.0068 Residual | 705.5 14 50.3928571 R-squared = 0.4178 -------------+------------------------------ Adj R-squared = 0.3762 Total | 1211.75 15 80.7833333 Root MSE = 7.0988 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- at | -11.25 3.549396 -3.17 0.007 -18.8627 -3.637302 _cons | 8.75 2.509802 3.49 0.004 3.36701 14.13299 ------------------------------------------------------------------------------ . table at, contents( mean nc mean op mean diff ) op ambiguous abbreviation r(111); . table at, contents( mean nc mean opp mean diff ) ---------------------------------------------- at | mean(nc) mean(opp) mean(diff) ----------+----------------------------------- 0 | 84.125 75.375 8.75 1 | 74.75 77.25 -2.5 ---------------------------------------------- . log close log: T:\170\class1.log log type: text closed on: 3 Feb 2005, 12:14:56 ------------------------------------------------------------------------------------------------------
Posted by bparke at 07:35 PM | Comments (0)
February 01, 2005
Linear Regression
Posted by bparke at 09:13 PM | Comments (0)
Homework
Due 2/8; Chapter 6, #1,5,6,7
Posted by bparke at 12:28 AM | Comments (0)