Crystal Ball calculates sensitivity by computing rank correlation coefficients between every assumption and every forecast while the simulation is running. Correlation coefficients provide a meaningful measure of the degree to which assumptions and forecasts change together. If an assumption and a forecast have a high correlation coefficient, it means that the assumption has a significant impact on the forecast (both through its uncertainty and its model sensitivity). Positive coefficients indicate that an increase in the assumption is associated with an increase in the forecast. Negative coefficients imply the opposite situation. The larger the absolute value of the correlation coefficient, the stronger the relationship.
To help interpret the rank correlations, Crystal Ball provides a default chart view called the Contribution To Variance view. This view makes it easier to answer questions such as "What percentage of the variance or uncertainty in the target forecast is due to assumption X?"
It is important to note that the Contribution To Variance method is only an approximation and is not precisely a variance decomposition. Crystal Ball calculates Contribution To Variance by squaring the rank correlation coefficients and normalizing them to 100%.
Both the alternate Rank Correlation View and the Contribution To Variance view display the direction of each assumption’s relationship to the target forecast. Assumptions with a positive relationship have bars on the right side of the zero line. Assumptions with a negative relationship have bars on the left side of the zero line. To show just the absolute magnitude of the relationship, you can change the Chart Type preference setting described in Table 8 to Bar (Magnitude).