I will downvote and report if not correct or copied from anywhere"

3. Fit a logistic regression model using these variables. Use DRINK as the dependent variable and CASES and SEX as independent variables. Also include as an independent variable the appropriate interaction term. Some variable creation commands .* Create 0/1 indicators of drinker and female gender generate drink01-. (294 missing values generated) replace drink01=1 if drink==1 (234 real changes made) replace drink01=0 if drink==2 (60 real changes made) . label define drinkf O "O=nondrinker" 1 "1-drinker label values drink01 drinkf generate female-. (294 values generated) replace female-o if sex==1 (111 real changes made) replace female=1 if sex==2 (183 real changes made) label define sexf O "Ormale" 1 "1=female" label values female sexf missing value .* Create a new variable called FEM_CASE that is the interaction of FEMALE and CASES generate fem_case=female cases * Use the command LOGISTIC if you want output to include ODDS RATIOS * Use the command LOGIT if you want the output to include BETAs and Ses * LOGISTIC OUTCOME PREDICTOR PREDICTOR etc.. . logit drink01 cases female fem_case Logistic regression Number of obs LR chi2 (3) Prob > chi2 Pseudo R2 294 5.62 0.1318 0.0189 Log likelihood = -145.95772 drink011 Coef. Std. Err. z P>z [95% Conf. Intervall cases female 1 fem case cons -.4405564 -.7743296 .9386327 1.826851 .8413815 .3455196 .9578851 .2879632 -0.52 -2.24 0.98 6.34 0.601 0.025 0.327 0.000 -2.089634 -1.451536 -.9387877 1.262453 1.208521 -.0971237 2.816053 2.391248