What Does Principal Component Analysis (PCA) Do?

Beyond changing basis and reducing dimension, the note asks what else the eigenbasis buys you — total variance surviving the rotation untouched, a gap between in-sample and out-of-sample variance explained signalling a shifted variance structure, and each loading reading as a linear regression coefficient.