Timeline for Why is correlation not appropriate in situations when regression is?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 14, 2018 at 20:02 | comment | added | Sextus Empiricus | I have seen Fisher describe regression as finding the relation f(X) that maximizes the correlation between Y and f(X). | |
| Nov 14, 2018 at 19:04 | answer | added | Acccumulation | timeline score: 2 | |
| Nov 11, 2016 at 18:20 | comment | added | ttnphns | They are primordially different conceptions, association and influence. Association is seen as symmetric (it is not about "effect"), influence is seen as directed. In regression, we typically perceive the predictor as error-free, and the predictand as model+error. In correlation, the model is on neither "side", it is bivariate, or, so to speak, on a side of some in-between "latent" variable, no special placement of error is typically indicated. In case of nonlinear association, X->Y and Y->X regressions may be quite different, while correlation (of a selected type) is one. | |
| Nov 10, 2016 at 21:59 | answer | added | Tim | timeline score: 6 | |
| Nov 10, 2016 at 21:27 | history | edited | z8080 | CC BY-SA 3.0 |
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| Nov 9, 2016 at 17:22 | history | asked | z8080 | CC BY-SA 3.0 |