Engine / Correlations

Correlations

Correlation handling for uncertainty and risk relationships, so independent-looking inputs do not create a false answer.

Engine / Correlations

Risks That Move Together, Modelled Together.

Treat every input as independent and the simulation quietly cancels risks against each other — the spread narrows and P80 looks better than it is. Set rank correlations on the pairs that share a driver, in a matrix or a heatmap.

15 dist · 5 pairs
DU-001RSK-0012RSK-0008RSK-0011RSK-0022RSK-0007RSK-0006RSK-0026
ScheduleProbabilityProbabilityScheduleProbabilityCostProbabilityScheduleProbabilityScheduleProbabilityScheduleProbabilityScheduleCost
DU-001Schedule1.0
RSK-0012Probability1.0
RSK-0008Probability1.0
Schedule1.0
RSK-0011Probability1.0
Cost1.0
RSK-0022Probability1.0
Schedule1.0
RSK-0007Probability1.0
Schedule1.0
RSK-0006Probability1.0
Schedule1.0
RSK-0026Probability1.0
Schedule1.0
Cost1.0

Correlation keeps related inputs moving together, so the simulation cannot quietly cancel risks against each other. Click any coefficient to change it — the heatmap and pair count follow. Hover a risk code for its ranges.

correlations / methodlive
01Rank (Spearman) correlation between distributions
02Iman-Conover style induced correlation in sampling
03Correlate schedule, cost and probability inputs
04Pair-level coefficients, not a blanket assumption
05Matrix and heatmap views of the same model
06Bulk-set coefficients across many pairs at once
07Filter rows and columns on large registers
08Stops uncertainty cancelling out and flattering P80
01 / At a Glance

What You Get

  • Rank correlation support
  • Iman-Conover style modelling
  • More realistic joint outcomes
02 / Next Step

See It on Your Own Project.

Start your Demo and run your first Monte Carlo analysis on your own schedule, or talk to sales and we will take you through it on a sample project. No card needed.

Talk to Sales