# Using Principal Component Analysis to determine variable interaction

I'm characterizing a communication channel by measuring bit error rate (ber) as a function of 15 channel settings: ber = Func(settings[1:15]). The dataset is about 100,000 samples.

I wonder if I can use PCA to explore interaction of settings and their effect on the bit error rate. Specifically:

1. How to prepare data for PCA; do I run PCA on {ber, settings[1:15]}, or only on settings[1:15] ? I've seen PCA used on "symmetric" data, such as images, text, etc.

2. What do projections of settings[1:15] on principal components tell me in this case

3. What do outlier datapoints in principal component space tell me.

Principal Component Analysis is an unsupervised method -- that is, you use it on independent variables, settings in your case.