Description: Adjusted R-squared is a modified version of R-squared that accounts for the number of predictors in a regression model. While R-squared measures the proportion of variability in the dependent variable explained by the predictors, Adjusted R-squared adjusts this measure to penalize the inclusion of additional predictors that do not significantly improve the model. This is particularly useful in models with multiple variables, as R-squared can increase simply by adding more variables, regardless of their relevance. Adjusted R-squared provides a more accurate assessment of model quality, as it only increases if the inclusion of a new predictor improves the model beyond what would be expected by chance. Its value can be lower than the original R-squared, indicating that the model has not been adequately fitted to the data. In summary, Adjusted R-squared is an essential tool for analysts and statisticians looking to build robust regression models and avoid overfitting, allowing for better interpretation of the relationship between variables.
