Statistical Test


Quick question. I'm taking a Stats & Computation Module particularly to do with R. I have a basic knowledge of Statistics, however I do agree it is one of my weaker areas. I'm currently attempting to perform a statistical analysis based on a predator-prey model I have built.

My hypothesis I wish to test is if continuous searching of a predator provides an advantage over discontinuous. In the context of my model, I applied it as such. I have a set number of prey (25) that remains constant throughout my entire data set. I then measured about 13 different sets of data. For instance, I made my predator continue searching constantly (wait time = 0), measured the total time taken to consume all prey and then repeated this 10 times. I then altered the wait time (wait time = 0.25s), measured 10 trials and repeated so on so forth. I measured up to 3 seconds in 0.25 second intervals, with 10 replicates of each (i.e. 13 overall groups (wait time = 0, 0.25, 0.5, 0.75 etc. etc.).

I have my data, and I understand the coding behind R, however I'm stumped on the approach I should take for my test. My first instinct was to perform a regression test on the averages of each of these wait time groups, and see if there's any correlation between wait time and overall time. Would I be correct in going down this path? Is there a more sensible test I should consider using?