-------------------------------------------------------------------------------- log: C:\Documents and Settings\Patricia_Anderson\My Documents\ECON 20\In > Class\nov6.log log type: text opened on: 6 Nov 2002, 11:14:23 . use pntsprd . **lpm on basketball . reg favwin spread Source | SS df MS Number of obs = 553 -------------+------------------------------ F( 1, 551) = 68.57 Model | 11.0636261 1 11.0636261 Prob > F = 0.0000 Residual | 88.9038241 551 .161349953 R-squared = 0.1107 -------------+------------------------------ Adj R-squared = 0.1091 Total | 99.9674503 552 .181100453 Root MSE = .40168 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .0193655 .0023386 8.28 0.000 .0147718 .0239593 _cons | .5769492 .0282345 20.43 0.000 .5214888 .6324097 ------------------------------------------------------------------------------ . reg favwin spread, robust Regression with robust standard errors Number of obs = 553 F( 1, 551) = 101.54 Prob > F = 0.0000 R-squared = 0.1107 Root MSE = .40168 ------------------------------------------------------------------------------ | Robust favwin | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .0193655 .0019218 10.08 0.000 .0155905 .0231405 _cons | .5769492 .0316568 18.23 0.000 .5147664 .6391321 ------------------------------------------------------------------------------ . predict probwin (option xb assumed; fitted values) . graph probwin spread . **do a probit . probit favwin spread Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -266.49244 Iteration 2: log likelihood = -263.62542 Iteration 3: log likelihood = -263.56223 Iteration 4: log likelihood = -263.56219 Probit estimates Number of obs = 553 LR chi2(1) = 78.38 Prob > chi2 = 0.0000 Log likelihood = -263.56219 Pseudo R2 = 0.1294 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .092463 .0121811 7.59 0.000 .0685885 .1163374 _cons | -.0105926 .1037469 -0.10 0.919 -.2139328 .1927476 ------------------------------------------------------------------------------ . dprobit favwin spread Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -266.49244 Iteration 2: log likelihood = -263.62542 Iteration 3: log likelihood = -263.56223 Iteration 4: log likelihood = -263.56219 Probit estimates Number of obs = 553 LR chi2(1) = 78.38 Prob > chi2 = 0.0000 Log likelihood = -263.56219 Pseudo R2 = 0.1294 ------------------------------------------------------------------------------ favwin | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- spread | .0250833 .0028961 7.59 0.000 9.61302 .019407 .03076 ---------+-------------------------------------------------------------------- obs. P | .7631103 pred. P | .8100975 (at x-bar) ------------------------------------------------------------------------------ z and P>|z| are the test of the underlying coefficient being 0 . **add a dummy . dprobit favwin spread fav25 favhome Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -265.94671 Iteration 2: log likelihood = -263.08924 Iteration 3: log likelihood = -263.02473 Iteration 4: log likelihood = -263.02468 Probit estimates Number of obs = 553 LR chi2(3) = 79.45 Prob > chi2 = 0.0000 Log likelihood = -263.02468 Pseudo R2 = 0.1312 ------------------------------------------------------------------------------ favwin | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- spread | .0245723 .0030216 7.18 0.000 9.61302 .01865 .030495 fav25*| -.0121475 .041001 -0.30 0.765 .264014 -.092508 .068213 favhome*| .0342042 .0377794 0.92 0.356 .678119 -.039842 .10825 ---------+-------------------------------------------------------------------- obs. P | .7631103 pred. P | .8103957 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| are the test of the underlying coefficient being 0 . test fav25 favhome ( 1) fav25 = 0.0 ( 2) favhome = 0.0 chi2( 2) = 1.08 Prob > chi2 = 0.5832 . reg favwin spread fav25 favhome Source | SS df MS Number of obs = 553 -------------+------------------------------ F( 3, 549) = 23.35 Model | 11.3103676 3 3.77012253 Prob > F = 0.0000 Residual | 88.6570827 549 .161488311 R-squared = 0.1131 -------------+------------------------------ Adj R-squared = 0.1083 Total | 99.9674503 552 .181100453 Root MSE = .40186 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .0186401 .0024634 7.57 0.000 .0138013 .0234789 fav25 | -.0052219 .0396048 -0.13 0.895 -.0830173 .0725735 favhome | .0458355 .0383373 1.20 0.232 -.0294703 .1211413 _cons | .5542195 .0358419 15.46 0.000 .4838154 .6246236 ------------------------------------------------------------------------------ . test fav25 favhome ( 1) fav25 = 0.0 ( 2) favhome = 0.0 F( 2, 549) = 0.76 Prob > F = 0.4663 . probit favwin spread Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -266.49244 Iteration 2: log likelihood = -263.62542 Iteration 3: log likelihood = -263.56223 Iteration 4: log likelihood = -263.56219 Probit estimates Number of obs = 553 LR chi2(1) = 78.38 Prob > chi2 = 0.0000 Log likelihood = -263.56219 Pseudo R2 = 0.1294 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .092463 .0121811 7.59 0.000 .0685885 .1163374 _cons | -.0105926 .1037469 -0.10 0.919 -.2139328 .1927476 ------------------------------------------------------------------------------ . predict probwin2 (option p assumed; Pr(favwin)) . graph probwin probwin2 spread . **try a logit . logit favwin spread Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -268.51377 Iteration 2: log likelihood = -264.1308 Iteration 3: log likelihood = -263.90218 Iteration 4: log likelihood = -263.90131 Iteration 5: log likelihood = -263.90131 Logit estimates Number of obs = 553 LR chi2(1) = 77.70 Prob > chi2 = 0.0000 Log likelihood = -263.90131 Pseudo R2 = 0.1283 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .1632261 .0225567 7.24 0.000 .1190158 .2074364 _cons | -.071157 .1732172 -0.41 0.681 -.4106566 .2683425 ------------------------------------------------------------------------------ . **get the marginal effects . mfx compute Marginal effects after logit y = Pr(favwin) (predict) = .81726683 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- spread | .0243765 .00269 9.06 0.000 .019103 .02965 9.61302 ------------------------------------------------------------------------------ . predict probwin3 (option p assumed; Pr(favwin)) . graph probwin probwin2 probwin3 spread . probit favwin spread Iteration 0: log likelihood = -302.74988 Iteration 1: log likelihood = -266.49244 Iteration 2: log likelihood = -263.62542 Iteration 3: log likelihood = -263.56223 Iteration 4: log likelihood = -263.56219 Probit estimates Number of obs = 553 LR chi2(1) = 78.38 Prob > chi2 = 0.0000 Log likelihood = -263.56219 Pseudo R2 = 0.1294 ------------------------------------------------------------------------------ favwin | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread | .092463 .0121811 7.59 0.000 .0685885 .1163374 _cons | -.0105926 .1037469 -0.10 0.919 -.2139328 .1927476 ------------------------------------------------------------------------------ . mfx compute Marginal effects after probit y = Pr(favwin) (predict) = .81009755 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- spread | .0250833 .0029 8.66 0.000 .019407 .03076 9.61302 ------------------------------------------------------------------------------ . sum favwin Variable | Obs Mean Std. Dev. Min Max -------------+----------------------------------------------------- favwin | 553 .7631103 .425559 0 1 . ***alternate rule of thumb . .76*.24*.16 unrecognized command: .76 invalid command name r(199); . display .76*.24*.16 .029184 . use affairs, clear . dprobit affair yrsmarr vryhap vryrel Iteration 0: log likelihood = -337.68849 Iteration 1: log likelihood = -322.24412 Iteration 2: log likelihood = -322.1449 Iteration 3: log likelihood = -322.14488 Probit estimates Number of obs = 601 LR chi2(3) = 31.09 Prob > chi2 = 0.0000 Log likelihood = -322.14488 Pseudo R2 = 0.0460 ------------------------------------------------------------------------------ affair | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- yrsmarr | .0088336 .0033055 2.66 0.008 8.1777 .002355 .015312 vryhap*| -.1444131 .035005 -3.89 0.000 .386023 -.213022 -.075805 vryrel*| -.0822315 .0485325 -1.54 0.125 .116473 -.177354 .012891 ---------+-------------------------------------------------------------------- obs. P | .249584 pred. P | .2380589 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| are the test of the underlying coefficient being 0 . dprobit affair yrsmarr vryhap vryrel male kids Iteration 0: log likelihood = -337.68849 Iteration 1: log likelihood = -320.97216 Iteration 2: log likelihood = -320.82988 Iteration 3: log likelihood = -320.82984 Probit estimates Number of obs = 601 LR chi2(5) = 33.72 Prob > chi2 = 0.0000 Log likelihood = -320.82984 Pseudo R2 = 0.0499 ------------------------------------------------------------------------------ affair | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- yrsmarr | .0063473 .0038295 1.66 0.098 8.1777 -.001158 .013853 vryhap*| -.1371368 .0354211 -3.66 0.000 .386023 -.206561 -.067713 vryrel*| -.0823149 .0483765 -1.54 0.123 .116473 -.177131 .012501 male*| .0337697 .0352789 0.96 0.338 .475874 -.035376 .102915 kids*| .0604192 .0457933 1.27 0.203 .715474 -.029334 .150172 ---------+-------------------------------------------------------------------- obs. P | .249584 pred. P | .2367979 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| are the test of the underlying coefficient being 0 . test male kids ( 1) male = 0.0 ( 2) kids = 0.0 chi2( 2) = 2.62 Prob > chi2 = 0.2699 . log close log: C:\Documents and Settings\Patricia_Anderson\My Documents\ECON 20\I > nClass\nov6.log log type: text closed on: 6 Nov 2002, 12:24:53 -------------------------------------------------------------------------------