Adjusted R-squared is a statistical measure used to evaluate the goodness of fit of a regression model. Unlike the regular R-squared, which can be artificially inflated by adding more predictors, Adjusted R-squared adjusts for the number of predictors in the model. This makes it a more reliable metric when comparing models with different numbers of independent variables. The value of Adjusted R-squared ranges from 0 to 1, where a higher value indicates a better fit. It helps researchers and analysts determine how well their model explains the variability of the dependent variable while penalizing unnecessary complexity.