I'm an engineer with an interest in statistics, and my company sent me to a 1 week training in PCA/PCR (principal component analysis/regression) using The Unscrambler. I'm starting to apply these analyses to data sets in my work, but I'm the most experienced person my company has in this analysis.
I understand that most data requires some amount of pre-processing, and what the different methods and types are, but how do I know if I've done a good job of preprocessing? Are there diagnostics for preprocessing? How do I tell an over-processed data set versus an under-processed set?
The pre-processing I was taught includes things like transformations, mean centering, variable scaling, normalization, derivatives, MSC/eMSC that are performed on the data set before the PCA or PCR is performed.
The first data set I'm working on is continuous process data for a PCR (how can we predict the outcome variable based on these (correlated) input variables). The second data set will be classification of different spectra (if the type of problem matters).