Question: PART 1: LOGISTIC REGRESSION 1) Estimate logistic regression model using buyer as dependent variable and following as predictor variables : last total_ gender child

PART 1: LOGISTIC REGRESSION 1) Estimate logistic regression model using "buyer " as dependent variable and following as predictor variables : last total_ gender child youth cook do_it reference art geog Below is the table and model using "buyer " as a dependent variable with the following as predictor variables : last total_ gender child youth cook do_it reference art geog generate female=(gender=="F") logistic buyer last total_ female child youth cook do_it refernce art geog Logistic regression Number of obs 50,000 LR chi2(10) 6233.25 Prob > chi2 0.00.0 Log likelihood = -12061.106 Pseudo R2 0.2053 buyer Odds Ratio Std. Err. Z P>|21 [95% Conf. Interval] last . 9096345 0025401 -33.92 0.000 9046696 . 9146266 total_ 1. 001117 0001984 5.63 0.000 1. 000728 1. 001506 female 4673296 0167121 -21.27 0.000 435696 50126 child . 8300941 . 0143461 -10.77 0.000 8024472 8586935 youth 8931734 0233197 -4.33 0.000 8486174 .9400687 cook 7631345 0130712 -15 . 78 0.000 7379406 7891885 do_it . 5832352 0157274 -19.99 0.000 . 5532105 . 6148894 refernce 1.264514 . 0335834 8.84 0.000 1.200375 1.332079 art 3. 175878 . 0703266 52. 18 0.000 3. 040988 3. 31675 geog 1.775845 . 033086 30.82 0.000 1. 712167 1. 841891 _cons . 2018744 0105173 -30.71 0.000 - 1822784 . 2235771 Note: _cons estimates baseline odds. 2) Summarize and interpret the results (so that a marketing manager can understand them ). Which variables are significant ? Which seem to be "important '? Interpret the odds - ratios for each of the predictors

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