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.