HomeTechniques and Tips@RISK Simulation: Numerical ResultsWhich Sensitivity Measure to Use?

6.22. Which Sensitivity Measure to Use?

Applies to: @RISK 5.x–7.x

@RISK gives me a lot of options for sensitivities in my tornado graph: correlation coefficients, regression coefficients, mapped regression coefficients, change in output mean, and so on. How do I choose an appropriate measurement in my situation?

After a simulation, the Sensitivity Analysis window is your handy overview of sensitivities for all outputs. In the Results section of the @RISK ribbon, click the small tornado to open the Sensitivity Analysis window. (You can also see most of this information by clicking the tornado at the bottom of a Browse Results window for an output.)

Regression or correlation coefficients:
Regression coefficients and regression mapped values are just scaled versions of each other. Correlation coefficients are rank-order correlation, which works well for linear or non-linear correlations. In the Sensitivity Analysis window, when you select Display Significant Inputs Using: Regression (Coefficients), @RISK will display R² ("RSqr") in each column. You can use R² to help you decide between correlation coefficients and regression coefficients:

For a more detailed explanation of correlation and regression, see Correlation Tornado versus Regression Tornado.

Change in output statistic:
The change in output statistic, added in @RISK 6, is an interesting, differencing approach to sensitivity. You can select mean, mode, or a particular percentile: click the % icon at the bottom of the Sensitivity Analysis window, or the tornado icon at the bottom of the Browse Results window and select Settings.

The Change in Output Statistic tornado displays a degree of difference for just the two extreme bins, but the spider shows more information: the direction of the relationship, and the degree of difference for every bin.

See also:

Last edited: 2016-04-20

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