What does the adjusted R-squared measure account for?

The adjusted R-squared measures the goodness of fit of a statistical model while accounting for the number of predictors in the model. Unlike the regular R-squared, which can increase as more variables are added regardless of their relevance, adjusted R-squared adjusts the statistic based on the number of predictors relative to the number of data points. This adjustment prevents overfitting by imposing a penalty for adding predictors that do not improve the model's explanatory power. Thus, it provides a more reliable indicator of how well the model generalizes when comparing models with different numbers of predictors.

While other options focus on different statistical calculations or concepts, they do not specifically relate to the particular function of the adjusted R-squared in reflecting the influence of the number of predictors on the model's performance.