Is there any difference between $R^2$ that we get from linear regression and pearson correlation?

marked as duplicate by Silverfish, hxd1011, Matthew Gunn, mdewey, gung regression Nov 7 '16 at 12:29

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  • $\sqrt{}$ is your friend :) – epsilone Nov 6 '16 at 23:38

A simple answer: in a simple regression model (with a single independent variable) where the independent is interval (not nominal) - Pearsons correlation$^2$ equals $R^2$. But in a multiple regression model it does not. Pearson correlation cannot control for confounders, while a regression model can, thus the explained variance of the dependent variable is a combination of the multiple independent variables' effect.

See: coefficient of determination

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