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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.The definition of R-squared is fairly straight-forward; it is the percentage of the response variable variation that is explained by a linear model.-squared = Explained variation / Total variation. R-squared is always between 0 and 100%: 1  0% indicates that the model explains none of the variability of the response data around its mean. 2  100% indicates that the model explains all the variability of the response data around its mean.
The adjusted R-squared is a modified version of R-squared for the number of predictors in a model. The adjusted R-squared can be negative, but isn't always, while an R-squared value is between zero and 100 and shows the linear relationship in the sample of data even when there is no basic relationship.The adjusted R-squared is the best estimate of the degree of relationship in the basic population.y Investopedia AAA |. A: One major difference between R-squared and the adjusted R-squared is that R-squared supposes that every independent variable in the model explains the variation in the dependent variable.