Cost Risk and Uncertainty Analysis Flashcards
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Read the first 6 Cost Risk and Uncertainty Analysis flashcards as text
What is 'program uncertainty' as distinguished from 'estimating uncertainty' in cost risk analysis?
Answer: Program uncertainty stems from unknown future events; estimating uncertainty stems from imprecision in the cost model itself
Program uncertainty reflects future events (requirements changes, technology immaturity) while estimating uncertainty reflects the analyst's limited knowledge of the true cost relationship.
Which term describes the practice of assigning probability distributions to cost estimating relationships (CERs) to reflect statistical uncertainty in the regression?
Answer: Parametric risk analysis
Parametric risk analysis applies uncertainty bounds derived from the CER's regression statistics (e.g., standard error) directly to the CER outputs in the Monte Carlo simulation.
In DoD cost risk analysis, what is the 'most likely cost' (MLC)?
Answer: The mode of the cost probability distribution
The most likely cost is the modal value—the peak of the probability density function—representing the single most probable cost outcome.
What is a 'risk register' and how does it relate to cost risk analysis?
Answer: A documented list of identified risks with their likelihood, impact, and mitigation strategies used to inform cost uncertainty ranges
The risk register captures identified program risks, and cost analysts use it to set the uncertainty ranges and correlation assumptions in the cost risk model.
Why is a log-normal distribution often preferred over a normal distribution for cost element uncertainty?
Answer: Because costs cannot be negative and log-normal is bounded at zero with a right-skewed tail
Cost growth is typically right-skewed (can overrun significantly but not underrun below zero), and the log-normal distribution naturally captures this behavior.
What does 'normalizing' data mean in the context of preparing inputs for a cost risk model?
Answer: Adjusting historical cost data to remove the effects of inflation, quantity, and other non-recurring factors before using it to set uncertainty ranges
Normalization ensures that historical comparisons are valid by placing all data on a common basis (same year dollars, same quantity, same scope) before deriving uncertainty parameters.