Monte Carlo Simulation

Correct Answer: Sensitivity analysis

Sensitivity analysis is the correct answer because it specifically refers to the process in Monte Carlo simulation that examines how different input variables affect the project outcomes. Sensitivity analysis helps project managers and analysts understand which variables have the most significant impact on project results by systematically evaluating correlations between inputs and outputs. This technique is essential for risk assessment and decision-making in project management. The other options are incorrect because: Scenario mapping and systematic correlation assessment is too broad and not the specific term used in Monte Carlo simulation for this process. Distribution parameter assessment focuses on defining the statistical distributions of variables rather than analyzing their relationships and impacts. Statistical variance decomposition is a different analytical technique that breaks down the variance of a model into components but is not the specific term used for identifying correlations between variables in Monte Carlo simulation.

Correct Answer: More extreme outcomes are likely to occur than would be expected in a normal distribution

In Monte Carlo simulations, kurtosis is a measure of the 'tailedness' of a probability distribution. When a distribution has high kurtosis, it indicates that the distribution has heavier tails and a higher, sharper peak compared to a normal distribution. This means there's a higher probability of extreme outcomes or events occurring. In project risk analysis, high kurtosis suggests that the project has a higher likelihood of experiencing extreme scenarios - both very positive or very negative outcomes - compared to what you'd expect in a normal distribution. This is important for risk assessment as it alerts project managers to prepare for these potential extreme scenarios. The other answers are incorrect because: - A perfectly symmetrical distribution across all scenarios doesn't necessarily indicate kurtosis - Consistent centering around the mean with minimal variation actually describes low kurtosis - A standard bell curve with equal weight in the tails describes a normal distribution with standard kurtosis, not high kurtosis

Correct Answer: To produce a probability distribution of outcomes based on multiple random variables

The primary purpose of Monte Carlo simulation is to produce a probability distribution of possible outcomes by running numerous iterations with random variables. This statistical method helps understand the impact of risk and uncertainty in project management. The correct answer accurately describes how Monte Carlo works by generating multiple scenarios using random sampling to get a full range of possible outcomes and their probabilities. The other options are incorrect because: - Calculating average duration is just one possible application, not the key purpose - Creating a historical database is not related to Monte Carlo simulation's core function - Evaluating all possible risk combinations would be impractical and is not how Monte Carlo works - it uses random sampling instead of testing every possibility Monte Carlo simulation's strength lies in its ability to model uncertainty by running thousands of iterations with different random variables, producing a statistical distribution that helps in understanding potential outcomes and their likelihood.