I am running a large number of simulations in which I have a 3D parameter space, and for each set of parameters (point in the 3D space) I run 100 simulations. I then use 14 measures to quantify the results of the simulations, so for each point in parameter space I have the mean and standard deviation of each of these 14 measures.
I want to test which of the 14 measures are most useful for discriminating between my 3 initial parameters, so I'm using Principal Component Analysis. I can discriminate pretty well between all the mean values of my measures using 2 or 3 principal components. My question is, how do I convert the standard deviations on each of the measures at each point into principal component space so I can tell if they are well spaced enough to differentiate between the different parameters?
Also, I'm an astrophysicist, so please keep your maths clear and explanations free of jargon/unexplained symbols.