Sharing a great explanation of PCA

PCA analysis was the beginning of my spatiotemporal data analysis journey and went all the way through my PhD study. It can be understood simply as an orthogonal, eigen-decomposition of covaraince matrix, with the variance of each component arranged in decreasing order, however, the links between it and linear regression, ANOVA, etc. are not imprinted in mind and it turned out I kept feeling not understanding it completely and trying to demystify it. Now I found the best illustration that explains my confusion, enjoy reading!

https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues

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