When the values of two variables depend on each other in any way, you should correlate them to increase the accuracy of the simulation's forecast results.
There are two types of correlations:
Positive correlation — Indicates that two assumptions increase or decrease together. For example, the price of gasoline and shipping costs increase and decrease together.
Negative correlation — Indicates that an increase in one assumption results in a decrease in the other assumption. For example, the more items you buy from a particular vendor, the lower the unit cost.
The correlation coefficient range is -1 to 1, where 0 indicates no correlation. The closer the coefficient is to ±1, the stronger the relationship between the assumptions. You should never use a coefficient of ±1; represent relationships this closely correlated with formulas in the spreadsheet model.