Knowledge base

Single-factor sensitivity is a useful way to understand risk in a model. You can look at single-factor sensitivity using the R-squared (R²) value from a linear regression; this is also called the coefficient of determination.

The R² value will tell you how much of the variance in the model is explained by variance in one or more of the inputs to the model. The add-in function SimulationRSquared returns the R² statistic for an independent value and a dependent value.

The independent value is the input — generally one of the random variables in your model. The dependent value is the output — the result of the model.

Another way to think about this is that the result depends on the input.

Use the SimulationRSquared function to create a table of R² values for your model, and then add a donut chart for a visual representation.

For more, see wikipedia.