Stats 3.5Concept6 parts

Monte Carlo Simulation

Approximate an expected outcome when several uncertain inputs interact and the calculation is hard to do by hand.

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In this lesson6 parts

  1. 01Turn recorded sales into an explicit simulation input
  2. 02Check the resampling method against a known fraction
  3. 03Build one two-day outcome and cost
  4. 04Repeat identical draws for four candidate levels
  5. 05Test what the answer depends on
  6. 06Reproduce the calculation and hand off the decision

Key terms

The words this lesson introduces, each in one line. The module’s glossary collects them all.

input distribution
The values a simulation is allowed to draw under its model: the 334 recorded daily totals of item 85123A.
zero recorded sales
No included positive item units in this file on that date (57 days); not zero demand.
resampling with replacement
Each draw uses the same source values, so repeats are possible.
Monte Carlo simulation
Repeated random draws and outcome calculations under stated assumptions.
Monte Carlo error
Difference caused by using finitely many random draws under a fixed model: 30.44% simulated against 29.94% exact.
stockout
The simulated total exceeds the candidate stock.
leftover
The candidate stock exceeds the simulated total.
assumption uncertainty
Uncertainty about whether model inputs and rules describe the real decision; more runs do not reduce it.

Quiz 5 questions

Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.

Practice

Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.

The self-checking notebook for this lesson is Stats 3.5 (optional) · Monte Carlo simulation.

Common mistakes

What you will see when it goes wrong, why it happens, and the fix.

Where it’s used

Where this lesson’s ideas turn up in real work.