Monte Carlo Simulation

Correct Answer: It uses random sampling to generate multiple scenarios based on probability distributions

Monte Carlo simulation is a powerful statistical method used in project risk analysis that relies on repeated random sampling to obtain numerical results. The correct answer accurately describes how Monte Carlo simulation functions - it generates multiple scenarios by using random sampling based on probability distributions of project variables. This allows project managers to understand the range of possible outcomes and their likelihood. The other options are incorrect because: - Using predefined scenarios based on historical data is a different approach that doesn't capture the probabilistic nature of Monte Carlo simulation - Monte Carlo simulation doesn't generate exact outcomes - it's about probability distributions and ranges of possible outcomes - The method doesn't create a single deterministic outcome - its strength lies in generating multiple possible scenarios to understand the range of potential outcomes and their probabilities

Correct Answer: The input variables have low uncertainty levels

In Monte Carlo simulation, the gap between Q1 and Q3 represents the interquartile range (IQR), which is a measure of variability in the data. When this gap remains consistently narrow across multiple iterations, it indicates that the input variables used in the simulation have low levels of uncertainty. A persistent narrow gap means the data points are clustered closely together, suggesting that the possible outcomes are fairly consistent and predictable. This points to input variables with low variance or uncertainty. The other answers are incorrect because: - Additional sampling points would not address a narrow Q1-Q3 gap; this is a characteristic of the underlying data, not the sample size. - Recalibration isn't needed when the gap is consistent across iterations; this actually shows the model is working as intended. - External factor correlation would typically cause variations in the Q1-Q3 gap rather than maintaining a consistent narrow spread.

Correct Answer: Periodic distribution with sinusoidal variation

When dealing with seasonal price fluctuations that follow cyclical patterns in Monte Carlo simulation, a periodic distribution with sinusoidal variation is the most appropriate choice because: 1. It naturally models repetitive patterns that occur at regular intervals 2. Sinusoidal functions inherently capture the cyclical nature of seasonal variations 3. It can accurately represent both peaks and troughs in the price cycle 4. It maintains mathematical consistency with the periodic nature of seasons The exponential distribution is more suitable for modeling time between events or continuous growth scenarios, not cyclical patterns. A normal distribution, while useful for many scenarios, does not capture the repetitive nature of seasonal cycles, even with historical data adjustments. The beta distribution is better suited for modeling variables with fixed minimum and maximum values, particularly when dealing with uncertainty in expert estimates, but does not inherently handle cyclical patterns.