Adj R Square. The default adjusted R-squared estimator has the disadvantage of not being unbiased. The amount of variance explained is widely reported for quantifying the model fit of a multiple linear regression model.
R-squared measures the proportion of the variation in your dependent variable Y explained by your independent variables X for a linear regression model. Some literature says the value ranges from 1 to 100. In this case SStot measures total variation.
If the number of predictors is greater than 1 Adj-square is.
The value for R-squared can range from 0 to 1. If we had a really low RSS value it would mean that the regression line was very close to the actual points. In this case SStot measures total variation. R-square is a modified version of R-square which is adjusted for the number of predictor in the fitted line.
