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get_ci_for_value(experiment_df.rename( columns = {'customer_value': 'predicted_value'}), 1000, 0.95) # (16.28, 34.63) Similarly, we can use logistic regression for retention analysis. retention_model = smf.logit('retention ~ treatment + num_family_members', data = experiment_df).fit(disp = 0)