1) The R-squared of a linear regression model is equal to the proportion of the variance of the target explained by a linear regression model.
2). The R-squared of a linear regression model is equal to the proportion of the variance of the predictors explained by a linear regression model.
3). R-squared is always between 0 and 1
4). For a given dataset the linear regression model with the lowest RSS, has the highest R-squared
5). Adding a predictor which is uncorrelated with the target reduces R-squared due to overfitting.