Dear all; I'm looking for some advice regarding the following idea: Let's say that I have a sample of y-values randomly taken from a population and I want to compute the mean of y and its confidence intervals but without assuming any particular distribution (I'm assuming that the mean of this sample is a good indicator of the mean of the population of y's). As far as I know we can use a nonparametric bootstrap analysis approach to do something like this. Now, let's say that instead of having to measure "y", I can measure "x" because is easier. Moreover I have a model that relates y and x, so I can predict the "y" giving the set of observed x. At the end of the day I will have yhat=(y1-hat,...,yn-hat)' which is the vector of predicted y-values. Here the is question: Does it make any sense to try to calculate the mean of the predicted "y's" and its CI by using a bootstrap analysis? Am I violating any assumptions for that kind of analysis? (maybe the independence of the samples?) Sorry if this is a dumb question but I would like to have a different opinion Thanks in advance PM