Certified Government Financial Manager (CGFM) Practice Exam

The choice indicating that data ranges must be relevant to the analysis accurately reflects a key aspect of regression analysis. In order for the results of a regression model to be valid and reliable, the data used must be applicable to the context of the analysis. If the ranges of the data do not pertain to the scenarios being assessed, any conclusions drawn may be misleading or incorrect.

For example, using historical data that doesn't reflect current conditions can skew predictions about future trends. The relevance of data ranges ensures that the relationships identified in the model are meaningful and can be properly understood and applied to real-world situations. This relevance is crucial for the integrity of the model, as it directly impacts the findings' applicability and efficacy.

In contrast, other choices do not encapsulate fundamental limitations as effectively. While having high correlation coefficients can indicate a strong relationship, they do not necessarily imply that the relationship is significant or meaningful in practical terms. Similarly, while larger sample sizes can enhance the robustness of findings, they aren't inherently a limitation of regression analysis since adequate sample sizes vary based on the context of the analysis. The claim that correlation is equivalent to causation is a common misconception but isn't a limitation of regression analysis itself; rather, it's a caution regarding the interpretation of results.