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Some estimates are linked. If demand is high, price may be high too. If you treat them as independent, the model assumes a high-demand future and a low price can happen as often as a high-demand future with a high price. That usually makes the spread of outcomes look different from reality.

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Setting a correlation

You can say how strongly two estimates move together, or calculate it from your own data.
  • A positive correlation means they tend to be high together and low together.
  • A negative correlation means one tends to be high when the other is low.
  • Zero means no link, which is the default.

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The app checks the whole set

A group of correlations has to make sense together. If it does not, the app explains why and suggests the nearest consistent values for you to accept. It never quietly “fixes” your numbers.

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When it matters

  • Inputs that share a cause (market size and demand, volume and revenue).
  • Costs that tend to overrun together.
If two inputs barely move the result, leave them independent. Next: Monte Carlo simulation