Comparing arrival rate distributions Given two samples of arrival rate of a process (they should end up something like a Poisson distribution):


*

*One over a long period (something like several days)

*Another from a shorter period time (maybe 30 minutes)


How can I compare the shorter distribution to see if it varies significantly from the longer distribution? Ideally using an existing function in R?
I believe a Q-Q Plot is showing this visually, but I am looking for a single number that would represent how far the shorter sample is from the longer sample.
Also, perhaps better suited for another question. I am a bit curious about how I might say what amount of time would be a representative sample for the longer period of time.
For reference the longer period plot looks something like the following, but it might very somewhat as I decide which attributes I might want to filter out:

 A: The ks.test (Kolmogorov-Smirnov test) function in R will return a p-value for a two-sample test of the null hypothesis that x and y were drawn from the same continuous distribution, where x and y are would be your two samples.  However, if the number of observations in the shorter data set is small, the test will have very little power, i.e., ability to identify situations where the underlying distributions are actually different.  
I assume your second question refers to the sample size needed to generate a representative sample?  This depends upon the arrival process itself and upon your criterion for "representative."  If the arrival process is indeed Poisson, the distribution is characterized by the mean, so the criterion would, in effect, be how accurately you can estimate the mean of the process.  Given a rough estimate of the mean and the criterion, you can calculate an estimate of the sample size necessary to achieve whatever accuracy you want.  
Maybe you could expand on this question?
