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• 例16-01计算结果.doc

  例题16-01(例): logit y x1 x2 [fw=f]Iteration 0: log likelihood = -61398392Iteration 1: log likelihood = -57977863Iteration 2: log likelihood = -57971109Iteration 3: log likelihood = -57971109Logistic r

• 例02-16计算结果.doc

  例题02-16（例 ） sumRBCVariable | ObsMeanStd Dev MinMax-------------+-------------------------------------------------------- RBC | 13842270294457298 307 546 di r(mean)-196*r(sd)33533986 di r(mean)+196*r(sd)51006594

• 例16-04计算结果.doc

  例题16-04(例): ologit y x1 x2 [fw=f]Iteration 0: log likelihood = -84957958Iteration 1: log likelihood =-7513773Iteration 2: log likelihood =-7501497Iteration 3: log likelihood = -75014707Ordered logis

• 例16-03计算结果.doc

  例题16-03(例): sw clogit y x1-x6, group(i) pe(01) pr(015) forwardbegin with empty modelp = 00017 01000adding x2p = 00078 01000adding x3p = 00521 01000adding x6p = 00392 01000adding x4Conditional (fixed

• 例16-02计算结果.doc

  例题16-02(例): sw logit y x1-x8, pe(01) pr(015) forwardbegin with empty modelp = 00081 01000adding x5p = 00075 01000adding x6p = 00340 01000adding x8p = 00525 01000adding x1Logistic regression Number o

• 例16-05计算结果.doc

  例题16-05(例): mlogit y x1 x2 [fw=f], basecategory(1)Iteration 0: log likelihood = -33860345Iteration 1: log likelihood =-3168858Iteration 2: log likelihood = -316576Iteration 3: log likelihood =-31657

• 例08-01计算结果.doc

  例 08-01 （例 ） signrankold=new ifold~=newWilcoxon signed-rank testsign |obs sum ranksexpected-------------+---------------------------------positive |211533negative |954533zero |0 0 0-------------+---

• 例17-01计算结果.doc

  例题17-01(例): stset time,failure(status) failure event:status != 0 & statusobs time interval:(0, time] exit on or before:failure------------------------------------------------------------------------

• 例03-01计算结果.doc

  例 03-01 drop _all set seed 0 set obs 10obs was 0, now 10 quietlyfor num 1/100:gen varX=invnorm(uniform())*53+1677 format var* %92f ci var*Variable |ObsMeanStd Err [95% Conf Interval]-------------+--

• 例15-01计算结果.doc

  例题15-01（例): regress y x*Source | SS df MSNumber of obs =27-------------+------------------------------ F(4,22) =828 Model |133710679 4334276697 ProbF=00003Residual |88841159222403823451 R-squared =0