I like the goal, thanks for the presentation. Personally, I'd have preferred an example on high-dimensionality data like curve fitting where this is actually most important, but that's because I'm a nerd and I like graphs perhaps.<p>FTIR data analysis is a fantastic example for PCA analysis -- each principle factor ends up (probably) being the spectrum of one of the major real physical components. But this is maybe too abstract?<p>A less abstract one might be a distribution of test scores. Your actual dataset is "number" versus "score", and you could show two gaussians, one at a low number and one at a high number. Then you could show that across three exams, you always see the same scores, but with different intensities. That would let you compute that the principle components are those two gaussians. Then you can hypothesize that each group is a collection of students that study together, and so they get similar scores. Or something like that.<p>Anyway, no intent to be a wet blanket. It's a nice writeup, and it is nice of you to share.