What does an increase in R2 imply in regression analysis?

An increase in R-squared (R²) in regression analysis indicates a better fit of the model to the data. R-squared is a statistical measure that represents the proportion of variance for a dependent variable that's explained by the independent variables in a regression model. When R² increases, it means that a larger percentage of the variability in the dependent variable can be accounted for by the independent variables present in the model.

This increased explanatory power enhances the model's ability to describe how the independent variables affect the dependent variable. Consequently, a higher R² value suggests that the model explains the data more effectively, leading to better predictions and insights.

While an increase in R² is generally seen as positive, it can also raise concerns about overfitting if the model becomes excessively complex. However, in the context of the question, the direct implication of a higher R² is that the model fit improves, making the first choice the most appropriate response.