In regard to the Regression node, what is typically provided in the Fit Statistics?

In the context of the Regression node within SAS Enterprise Miner, Fit Statistics are designed to give an overview of how well the regression model fits the data. One of the critical components of these statistics is information that affects the estimated predictions, such as R-squared, adjusted R-squared, and root mean square error. These metrics help quantify the model's explanatory power and predictive capabilities, allowing users to assess the effectiveness of the fitted model.

For instance, R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables, while the adjusted R-squared provides a more accurate measure by adjusting for the number of predictors in the model. Root mean square error represents the average error of the predictions and helps in understanding how close the predicted values are to the actual values.

While fit statistics do include other relevant information, the primary focus surrounds the metrics that directly inform about the effectiveness of the model in estimating predictions, which is why understanding these components is crucial for interpreting the regression results.