------------------------------------------------------------------------------------------------------------- log: d:\spost.stata8\do\st8ch3.log log type: text opened on: 26 May 2003, 12:48:27 . . // * . // * RM4STATA Ch 3: Estimation, Testing and Fit - 5/26/2003 . // * . . // * Section 3.1.1: stata's output for ML estimation . . use binlfp2, clear (Data from 1976 PSID-T Mroz) . logit lfp k5 k618 age wc hc lwg inc Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -454.32339 Iteration 2: log likelihood = -452.64187 Iteration 3: log likelihood = -452.63296 Iteration 4: log likelihood = -452.63296 Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . . // * Section 3.1.4: syntax of estimation commands . . use binlfp2, clear (Data from 1976 PSID-T Mroz) . logit lfp k5 k618 age wc lwg Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -463.65649 Iteration 2: log likelihood = -462.65715 Iteration 3: log likelihood = -462.6543 Logit estimates Number of obs = 753 LR chi2(5) = 104.44 Prob > chi2 = 0.0000 Log likelihood = -462.6543 Pseudo R2 = 0.1014 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.446702 .1936315 -7.47 0.000 -1.826212 -1.067191 k618 | -.0888295 .066761 -1.33 0.183 -.2196787 .0420197 age | -.0679554 .0124646 -5.45 0.000 -.0923856 -.0435252 wc | .6111245 .1937169 3.15 0.002 .2314463 .9908026 lwg | .5586712 .1489251 3.75 0.000 .2667834 .850559 _cons | 2.87807 .6229111 4.62 0.000 1.657186 4.098953 ------------------------------------------------------------------------------ . logit lfp k5 k618 age wc lwg if hc==1 Iteration 0: log likelihood = -198.53844 Iteration 1: log likelihood = -180.86899 Iteration 2: log likelihood = -180.67769 Iteration 3: log likelihood = -180.67744 Logit estimates Number of obs = 295 LR chi2(5) = 35.72 Prob > chi2 = 0.0000 Log likelihood = -180.67744 Pseudo R2 = 0.0900 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.261881 .2703329 -4.67 0.000 -1.791723 -.7320378 k618 | -.1508599 .1047174 -1.44 0.150 -.3561021 .0543824 age | -.0357219 .0193139 -1.85 0.064 -.0735766 .0021327 wc | .5049629 .2671338 1.89 0.059 -.0186097 1.028535 lwg | .3090363 .2240291 1.38 0.168 -.1300526 .7481252 _cons | 1.85713 .9364245 1.98 0.047 .0217723 3.692489 ------------------------------------------------------------------------------ . . * hypothetical example of using weights: . * logit lfp k5 k618 age wc lwg [pweight=wgtvar] . . * using if and level options . logit lfp k5 k618 age wc lwg if hc==1, level(90) Iteration 0: log likelihood = -198.53844 Iteration 1: log likelihood = -180.86899 Iteration 2: log likelihood = -180.67769 Iteration 3: log likelihood = -180.67744 Logit estimates Number of obs = 295 LR chi2(5) = 35.72 Prob > chi2 = 0.0000 Log likelihood = -180.67744 Pseudo R2 = 0.0900 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [90% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.261881 .2703329 -4.67 0.000 -1.706539 -.8172225 k618 | -.1508599 .1047174 -1.44 0.150 -.3231046 .0213849 age | -.0357219 .0193139 -1.85 0.064 -.0674905 -.0039533 wc | .5049629 .2671338 1.89 0.059 .0655669 .9443588 lwg | .3090363 .2240291 1.38 0.168 -.0594587 .6775313 _cons | 1.85713 .9364245 1.98 0.047 .3168493 3.397412 ------------------------------------------------------------------------------ . . * dealing with (artifically created) missing data . use binlfp2, clear (Data from 1976 PSID-T Mroz) . replace k5 = . in 1/5 (5 real changes made, 5 to missing) . replace age = . in 20/30 (11 real changes made, 11 to missing) . replace k618 = . in 3/12 (10 real changes made, 10 to missing) . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 730 LR chi2(7) = 116.83 Prob > chi2 = 0.0000 Log likelihood = -436.65251 Pseudo R2 = 0.1180 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.476792 .2014244 -7.33 0.000 -1.871576 -1.082007 k618 | -.0684086 .0692892 -0.99 0.323 -.2042129 .0673956 age | -.0644248 .013123 -4.91 0.000 -.0901454 -.0387042 wc | .7764123 .2342069 3.32 0.001 .3173752 1.235449 hc | .1158278 .209483 0.55 0.580 -.2947513 .5264069 lwg | .5919411 .1514397 3.91 0.000 .2951248 .8887575 inc | -.0326607 .0085992 -3.80 0.000 -.0495149 -.0158065 _cons | 3.309676 .6609139 5.01 0.000 2.014309 4.605043 ------------------------------------------------------------------------------ . logit lfp k5 age wc hc lwg inc, nolog Logit estimates Number of obs = 737 LR chi2(6) = 118.48 Prob > chi2 = 0.0000 Log likelihood = -441.96048 Pseudo R2 = 0.1182 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.448563 .1968211 -7.36 0.000 -1.834325 -1.0628 age | -.058273 .0117218 -4.97 0.000 -.0812472 -.0352988 wc | .8116292 .233145 3.48 0.000 .3546733 1.268585 hc | .1240371 .2076742 0.60 0.550 -.2829967 .531071 lwg | .6069002 .1509179 4.02 0.000 .3111064 .9026939 inc | -.0334975 .0085175 -3.93 0.000 -.0501916 -.0168034 _cons | 2.915742 .5560157 5.24 0.000 1.825971 4.005513 ------------------------------------------------------------------------------ . . * using -mark- and -markmiss- to delete cases with missing data . mark nomiss . markout nomiss lfp k5 k618 age wc hc lwg inc . tab nomiss nomiss | Freq. Percent Cum. ------------+----------------------------------- 0 | 23 3.05 3.05 1 | 730 96.95 100.00 ------------+----------------------------------- Total | 753 100.00 . logit lfp k5 k618 age wc hc lwg inc if nomiss==1, nolog Logit estimates Number of obs = 730 LR chi2(7) = 116.83 Prob > chi2 = 0.0000 Log likelihood = -436.65251 Pseudo R2 = 0.1180 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.476792 .2014244 -7.33 0.000 -1.871576 -1.082007 k618 | -.0684086 .0692892 -0.99 0.323 -.2042129 .0673956 age | -.0644248 .013123 -4.91 0.000 -.0901454 -.0387042 wc | .7764123 .2342069 3.32 0.001 .3173752 1.235449 hc | .1158278 .209483 0.55 0.580 -.2947513 .5264069 lwg | .5919411 .1514397 3.91 0.000 .2951248 .8887575 inc | -.0326607 .0085992 -3.80 0.000 -.0495149 -.0158065 _cons | 3.309676 .6609139 5.01 0.000 2.014309 4.605043 ------------------------------------------------------------------------------ . logit lfp k5 age wc hc lwg inc if nomiss==1, nolog Logit estimates Number of obs = 730 LR chi2(6) = 115.86 Prob > chi2 = 0.0000 Log likelihood = -437.13987 Pseudo R2 = 0.1170 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.448468 .1981499 -7.31 0.000 -1.836835 -1.060102 age | -.0589012 .0117851 -5.00 0.000 -.0819995 -.0358029 wc | .788823 .2335963 3.38 0.001 .3309827 1.246663 hc | .1112957 .2089562 0.53 0.594 -.298251 .5208423 lwg | .6034854 .1509592 4.00 0.000 .3076108 .89936 inc | -.0331451 .0085629 -3.87 0.000 -.049928 -.0163622 _cons | 2.971067 .5598289 5.31 0.000 1.873822 4.068311 ------------------------------------------------------------------------------ . . // * Section 3.1.5: reading the output . . use binlfp2, clear (Data from 1976 PSID-T Mroz) . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . . // * Section 3.1.6: reformatting output with -estimates table- . . logit lfp k5 k618 age wc hc lwg, nolog Logit estimates Number of obs = 753 LR chi2(6) = 104.98 Prob > chi2 = 0.0000 Log likelihood = -462.38508 Pseudo R2 = 0.1019 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.438653 .1934591 -7.44 0.000 -1.817826 -1.05948 k618 | -.087358 .0667777 -1.31 0.191 -.2182399 .0435238 age | -.0687257 .0125147 -5.49 0.000 -.093254 -.0441974 wc | .6925291 .2236913 3.10 0.002 .2541021 1.130956 hc | -.1422436 .1940203 -0.73 0.463 -.5225164 .2380292 lwg | .5610326 .1488363 3.77 0.000 .2693188 .8527465 _cons | 2.93857 .6286731 4.67 0.000 1.706393 4.170746 ------------------------------------------------------------------------------ . estimates table, b(%9.3f) t label varwidth(30) -------------------------------------------- Variable | active -------------------------------+------------ # kids < 6 | -1.439 | -7.44 # kids 6-18 | -0.087 | -1.31 Wife's age in years | -0.069 | -5.49 Wife College: 1=yes 0=no | 0.693 | 3.10 Husband College: 1=yes 0=no | -0.142 | -0.73 Log of wife's estimated wages | 0.561 | 3.77 Constant | 2.939 | 4.67 -------------------------------------------- legend: b/t . . // * Section 3.1.7: alternative output with -listcoef- . . use science2, clear (Note that some of the variables have been artificially constructed.) . regress job female phd mcit3 fellow pub1 cit1 Source | SS df MS Number of obs = 161 -------------+------------------------------ F( 6, 154) = 7.74 Model | 28.8930452 6 4.81550754 Prob > F = 0.0000 Residual | 95.7559074 154 .621791607 R-squared = 0.2318 -------------+------------------------------ Adj R-squared = 0.2019 Total | 124.648953 160 .779055954 Root MSE = .78854 ------------------------------------------------------------------------------ job | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -.1243218 .1573559 -0.79 0.431 -.4351765 .1865329 phd | .2898888 .0732633 3.96 0.000 .145158 .4346196 mcit3 | .0021852 .0023485 0.93 0.354 -.0024542 .0068247 fellow | .1839757 .133502 1.38 0.170 -.0797559 .4477073 pub1 | -.0068635 .0255761 -0.27 0.789 -.0573889 .0436618 cit1 | .0080916 .0041173 1.97 0.051 -.0000421 .0162253 _cons | 1.763224 .2361352 7.47 0.000 1.296741 2.229706 ------------------------------------------------------------------------------ . listcoef female cit1, help regress (N=161): Unstandardized and Standardized Estimates Observed SD: .88264146 SD of Error: .78853764 ------------------------------------------------------------------------------- job | b t P>|t| bStdX bStdY bStdXY SDofX -------------+----------------------------------------------------------------- female | -0.12432 -0.790 0.431 -0.0534 -0.1409 -0.0605 0.4298 cit1 | 0.00809 1.965 0.051 0.1719 0.0092 0.1947 21.2422 ------------------------------------------------------------------------------- b = raw coefficient t = t-score for test of b=0 P>|t| = p-value for t-test bStdX = x-standardized coefficient bStdY = y-standardized coefficient bStdXY = fully standardized coefficient SDofX = standard deviation of X . . // * Section 3.3.1: Wald tests . . use binlfp2, clear (Data from 1976 PSID-T Mroz) . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . test k5 ( 1) k5 = 0 chi2( 1) = 55.14 Prob > chi2 = 0.0000 . test k5 k618 ( 1) k5 = 0 ( 2) k618 = 0 chi2( 2) = 55.16 Prob > chi2 = 0.0000 . test k5 k618 age wc hc lwg inc ( 1) k5 = 0 ( 2) k618 = 0 ( 3) age = 0 ( 4) wc = 0 ( 5) hc = 0 ( 6) lwg = 0 ( 7) inc = 0 chi2( 7) = 94.98 Prob > chi2 = 0.0000 . test k5=k618 ( 1) k5 - k618 = 0 chi2( 1) = 49.48 Prob > chi2 = 0.0000 . . * -accumulate- option . test k5=k618 ( 1) k5 - k618 = 0 chi2( 1) = 49.48 Prob > chi2 = 0.0000 . test wc=hc, accumulate ( 1) k5 - k618 = 0 ( 2) wc - hc = 0 chi2( 2) = 52.16 Prob > chi2 = 0.0000 . . // * Section 3.3.2: LR tests . . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . estimates store fmodel . logit lfp age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(5) = 58.00 Prob > chi2 = 0.0000 Log likelihood = -485.87551 Pseudo R2 = 0.0563 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0169788 .0096886 -1.75 0.080 -.0359682 .0020105 wc | .6524283 .2155619 3.03 0.002 .2299348 1.074922 hc | .0285808 .1954884 0.15 0.884 -.3545694 .4117311 lwg | .6157264 .1452656 4.24 0.000 .331011 .9004418 inc | -.0328025 .0076386 -4.29 0.000 -.0477739 -.0178311 _cons | .8098897 .4510786 1.80 0.073 -.0742082 1.693988 ------------------------------------------------------------------------------ . estimates store nmodel . lrtest fmodel nmodel likelihood-ratio test LR chi2(2) = 66.49 (Assumption: nmodel nested in fmodel) Prob > chi2 = 0.0000 . . // * Section 3.4: measures of fit . . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . fitstat Measures of Fit for logit of lfp Log-Lik Intercept Only: -514.873 Log-Lik Full Model: -452.633 D(745): 905.266 LR(7): 124.480 Prob > LR: 0.000 McFadden's R2: 0.121 McFadden's Adj R2: 0.105 Maximum Likelihood R2: 0.152 Cragg & Uhler's R2: 0.204 McKelvey and Zavoina's R2: 0.217 Efron's R2: 0.155 Variance of y*: 4.203 Variance of error: 3.290 Count R2: 0.693 Adj Count R2: 0.289 AIC: 1.223 AIC*n: 921.266 BIC: -4029.663 BIC': -78.112 . quietly fitstat, saving(mod1) . generate agesq = age*age . logit lfp k5 age agesq wc inc, nolog Logit estimates Number of obs = 753 LR chi2(5) = 106.44 Prob > chi2 = 0.0000 Log likelihood = -461.65276 Pseudo R2 = 0.1034 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.379839 .1954677 -7.06 0.000 -1.762949 -.9967297 age | .0568824 .11411 0.50 0.618 -.166769 .2805339 agesq | -.0012928 .001294 -1.00 0.318 -.0038291 .0012434 wc | 1.093673 .1987386 5.50 0.000 .7041522 1.483193 inc | -.0323176 .0077281 -4.18 0.000 -.0474645 -.0171707 _cons | .9791676 2.458098 0.40 0.690 -3.838616 5.796951 ------------------------------------------------------------------------------ . fitstat, using(mod1) Measures of Fit for logit of lfp Current Saved Difference Model: logit logit N: 753 753 0 Log-Lik Intercept Only: -514.873 -514.873 0.000 Log-Lik Full Model: -461.653 -452.633 -9.020 D: 923.306(747) 905.266(745) 18.040(2) LR: 106.441(5) 124.480(7) 18.040(2) Prob > LR: 0.000 0.000 0.000 McFadden's R2: 0.103 0.121 -0.018 McFadden's Adj R2: 0.092 0.105 -0.014 Maximum Likelihood R2: 0.132 0.152 -0.021 Cragg & Uhler's R2: 0.177 0.204 -0.028 McKelvey and Zavoina's R2: 0.182 0.217 -0.035 Efron's R2: 0.135 0.155 -0.020 Variance of y*: 4.023 4.203 -0.180 Variance of error: 3.290 3.290 0.000 Count R2: 0.677 0.693 -0.016 Adj Count R2: 0.252 0.289 -0.037 AIC: 1.242 1.223 0.019 AIC*n: 935.306 921.266 14.040 BIC: -4024.871 -4029.663 4.791 BIC': -73.321 -78.112 4.791 Difference of 4.791 in BIC' provides positive support for saved model. Note: p-value for difference in LR is only valid if models are nested. . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . lstat Logistic model for lfp -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 342 145 | 487 - | 86 180 | 266 -----------+--------------------------+----------- Total | 428 325 | 753 Classified + if predicted Pr(D) >= .5 True D defined as lfp != 0 -------------------------------------------------- Sensitivity Pr( +| D) 79.91% Specificity Pr( -|~D) 55.38% Positive predictive value Pr( D| +) 70.23% Negative predictive value Pr(~D| -) 67.67% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 44.62% False - rate for true D Pr( -| D) 20.09% False + rate for classified + Pr(~D| +) 29.77% False - rate for classified - Pr( D| -) 32.33% -------------------------------------------------- Correctly classified 69.32% -------------------------------------------------- . . // * Section 3.5.2: predictions using -predict- . . logit lfp k5 k618 age wc hc lwg inc, nolog Logit estimates Number of obs = 753 LR chi2(7) = 124.48 Prob > chi2 = 0.0000 Log likelihood = -452.63296 Pseudo R2 = 0.1209 ------------------------------------------------------------------------------ lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799 k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085 age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162 wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026 hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564 lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901 inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583 _cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092 ------------------------------------------------------------------------------ . predict pr1 (option p assumed; Pr(lfp)) . sum pr1 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- pr1 | 753 .5683931 .1944213 .0139875 .9621198 . . log close log: d:\spost.stata8\do\st8ch3.log log type: text closed on: 26 May 2003, 12:48:29 -------------------------------------------------------------------------------------------------------------