# Is there a difference between the $R^2$ statistic of a linear regression and pearson correlation? [duplicate]

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 StackExchange.ready(function() { if (StackExchange.options.isMobile) return; $('.dupe-hammer-message-hover:not(.hover-bound)').each(function() { var$hover = $(this).addClass('hover-bound'),$msg = $hover.siblings('.dupe-hammer-message');$hover.hover( function() { $hover.showInfoMessage('', { messageElement:$msg.clone().show(), transient: false, position: { my: 'bottom left', at: 'top center', offsetTop: -7 }, dismissable: false, relativeToBody: true }); }, function() { StackExchange.helpers.removeMessages(); } ); }); }); Nov 7 '16 at 12:29

• $\sqrt{}$ 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.