Which statement about making predictions using regression models is true?

When making predictions using regression models, the assumption that new observations follow the same model as the sample is fundamental. This statement underscores the essence of regression analysis, which is built on the idea that the relationships identified in the sample data will apply to broader populations or future observations. The model estimates the relationship between independent variables and the dependent variable based on the data it was trained on, and it is expected that new data points will conform to the same underlying pattern.

This allows analysts to make informed predictions, recognizing that while individual outcomes may vary due to inherent randomness, the overall trend predicted by the model should hold true for new observations that fall within the range of the original data. This assumption is crucial for the validity of any inferences made from the model.

In regard to the other options, they either misrepresent the nature or implications of regression models or highlight misconceptions about statistical predictions. The variability in predictions across candidate models suggests uncertainty in model selection, while the reliability of predictions isn't as straightforward as suggesting that point predictions are always superior. Further, the informativeness of prediction intervals depends on both their width and the context in which they are used.