Pointers on how to perform a Monte Carlo Analysis Im looking for some pointers on how to perform a Monte Carlo simulation. Say im conducting a survey of how late flights are for a certain airline. I amass 1000 records, and plot a cumulative sum of these values. I want to determine how likely it is to have several flights grouped together that a decrease in the cumulative sum would exceed X%. Would i
1) calculate the mean and standard deviation of this sample, use these to generate multiple samples, and record the probability of such a decrease? 
2) take the original sample, shuffle it, and then select values from it (without replacement), and record the probability of such a decrease?
 A: Both approaches need some modification.  If you do the first, in order to generate multiple samples from the mean and standard deviation you need to make assumptions about the distribution eg that it is Normal.  The approach will work (if your distributional assumption is sound), but adds little or nothing to traditional methods.  Only use it if you are satisfied you can characterise the distribution of times well - and this is highly unlikely to be a Normal distribution (Gamma is more likely) so you need the techiques to fit a non-normal distribution.
The second approach makes better use of the information contained in the empirical distribution of your sample, but it won't work unless your selection of values is done with replacement (rather than without replacement).  If you select without replacement you will just end up with your original sample of n once you have selected all n values from it.
The second approach is a form of the bootstrap, and I would encourage you to read some of the voluminous material available.
In both cases you need to assume that there is no information in the ordering of the times - for example, that one flight being late doesn't make the next flight late too.  If this isn't the case, you will need an alternative approach.
