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Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.

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How to interpret PCA loading and to relate it with correlation coefficient of associated ind...

On the first side, we are trying to run PCA. … This to me is surprising because V1 is very important when you look at it from PCA point of view but is very less important when you look at it from point of view of explaining DV. …
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