What kind of tables are useful for tweaking a finished model?

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Multiple Choice

What kind of tables are useful for tweaking a finished model?

Explanation:
Sensitivity analysis shows how outputs change when inputs move, which is exactly what you want after a model is finished. By laying out a table that varies a key assumption across a range while holding others steady, you can observe the resulting changes in metrics like NPV, IRR, cash flow, and debt service coverage. This lets you identify which assumptions the model is most sensitive to and where small changes could shift investment decisions. It also guides risk assessment and refinement, since you can focus on the inputs that move results the most. Other table types have different purposes: scenario tables compare a few inputs together to show futures under different combinations but don’t isolate the effect of a single variable; summary tables simply present outputs in a compact form; break-even tables highlight the threshold at which a metric turns positive or negative but don’t map a full range of inputs. So, when you want to fine-tune a finished model and understand which inputs matter most, sensitivity tables are the most direct and informative tool.

Sensitivity analysis shows how outputs change when inputs move, which is exactly what you want after a model is finished. By laying out a table that varies a key assumption across a range while holding others steady, you can observe the resulting changes in metrics like NPV, IRR, cash flow, and debt service coverage. This lets you identify which assumptions the model is most sensitive to and where small changes could shift investment decisions. It also guides risk assessment and refinement, since you can focus on the inputs that move results the most. Other table types have different purposes: scenario tables compare a few inputs together to show futures under different combinations but don’t isolate the effect of a single variable; summary tables simply present outputs in a compact form; break-even tables highlight the threshold at which a metric turns positive or negative but don’t map a full range of inputs. So, when you want to fine-tune a finished model and understand which inputs matter most, sensitivity tables are the most direct and informative tool.

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