Adjusted R squared is a statistical measure used to evaluate the goodness of fit of a regression model, particularly when dealing with multiple predictors. Unlike the standard R squared, which simply measures the proportion of the variance in the dependent variable explained by the independent variables, adjusted R squared adjusts for the number of predictors in the model. This adjustment is crucial because adding more variables to a model will always increase the R squared value, even if those variables do not have meaningful predictive power.
