I have seen several articles and CrossValidated questions on bootstrapping ( this, this or this for example); there are a lot of theoretical and statistical explanations, however since they are so theory based, I am afraid I might be understanding the use wrongly. Hence my questions:
1) When I make a non-parametric bootstrapping (changing the sample for every run) with logistic regression on my data, I basically will end up with several different coefficients for each predictor for each run. Eventually I'll have the confidence interval for each predictor as well. I understand until that point. My question is; assuming that the distribution is normal, when I want to come up with a final model on practice, can I just take the mean of the confidence intervals for each predictor and consider this as my final model coefficient?
2) If the answer to question #1 is yes, is this the only way of choosing coefficients while bootstrapping? If not, what else? I encountered in a few more articles a method called "bagging". This seems to be my main purpose.
3) This one is more of a curiosity question: Can above methodology be applied to the categorical predictors when they are assigned with Weight Of Evidences? I know we can split the categorical predictors into dummy variables; but how would I treat each coefficient if I want to use WOE methodology?