R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. R-squared is the percentage of the response variable variation that is explained by the model, it is always between 0 and 100%:
0% indicates that the model explains none of the variability of the response data around its mean
100% indicates that the model explains all the variability of the response data around its mean
In general, the higher the R-squared, the better the model fits your data. The biggest limitations are: R-squared cannot determine whether the coefficient estimates and predictions are biased, which is why you the residual plots need to be assessed, R-squared does not indicate whether a regression model is adequate. You can have a low R-squared value for a good model, or a high value for a model that does not fit the data.
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