R-Squared is most often used in linear regression. Given a set of data points, linear regression gives a formula for the line most closely matching those points. It also gives an R-Squared value to say how well the resulting line matches the original data points.
R-Squared is a statistical term saying how good one term is at predicting another. If R-Squared is 1.0 then given the value of one term, you can perfectly predict the value of another term. If R-Squared is 0.0, then knowing one term doesn’t not help you know the other term at all. More generally, a higher value of R-Squared means that you can better predict one term from another.
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