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What is the exact difference between principal component analysis (PCA)and principal component regression (PCR)? With what kind of data sets can one use these methods?

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Principal component analysis is a method of data reduction - representing a large number of variable by a (much) smaller number, each of which is a linear combination of the original variables.

One output of PCA is principal component scores. Principal component regression uses those scores as independent variables in a regression.

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  • $\begingroup$ is it possible explain me with matlab code or with example for example if i give one data set with these method what is different in out put of these methods ? $\endgroup$ – Fati Dec 1 '15 at 13:18
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    $\begingroup$ @fati See references in comment from Scortchi above. These methods are well documented here and elsewhere. Please focus on asking questions that are new here; otherwise the forum is a resource for your study. $\endgroup$ – Nick Cox Dec 1 '15 at 13:28
  • $\begingroup$ @Nick cox : i saw but i am not get well my answer . $\endgroup$ – Fati Dec 1 '15 at 13:30
  • $\begingroup$ I don't know Matlab but the output is completely different. PCA is a necessary step towards PC regression. But you can certainly do PCA without ever doing a regression. $\endgroup$ – Peter Flom - Reinstate Monica Dec 1 '15 at 23:49

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