Monte Carlo Simulation

Correct Answer: Run additional iterations to achieve higher statistical reliability

In Monte Carlo simulation, when confidence levels in convergence testing fall below 90%, the most appropriate action is to run additional iterations. Here's why this is correct: 1. Additional iterations increase the sample size 2. Larger sample sizes improve statistical reliability 3. More iterations help achieve better convergence 4. This follows standard statistical methodology for improving confidence levels Why other options are incorrect: - Applying sensitivity coefficients would alter the original data integrity and potentially introduce bias - Modifying sampling intervals could skew the results and reduce data accuracy - Switching probability distributions would be inappropriate as the distribution should be based on the actual data characteristics, not convergence issues The key principle is that Monte Carlo simulation accuracy improves with more iterations, making this the most reliable solution for improving confidence levels.

Correct Answer: The variables tend to move in opposite directions with a strong relationship

In Monte Carlo simulation, correlation coefficients range from -1 to +1. A correlation coefficient of -0.95 indicates a very strong negative correlation between two variables. When two variables have a correlation coefficient of -0.95, they exhibit an almost perfect inverse relationship. As one variable increases, the other variable decreases in a highly predictable manner. The correct answer recognizes this strong inverse relationship between the variables. A correlation of -0.95 means the variables move in opposite directions nearly all the time, with very few exceptions. The other options are incorrect because: - Synchronized parallel movement would indicate a positive correlation, not negative - Random relationships would show a correlation coefficient near zero - Moderate positive correlation contradicts the negative value of -0.95

Correct Answer: The number of iterations performed in the simulation

The number of iterations performed in a Monte Carlo simulation is the most critical parameter affecting the accuracy of tail probabilities in the output distribution for these reasons: A higher number of iterations provides: - Better representation of extreme events and rare scenarios - More reliable estimation of tail probabilities - Increased statistical confidence in the results - More stable convergence of the probability distributions The other options are less relevant because: The complexity of random number generation algorithms is standardized in modern software and rarely impacts accuracy significantly. Correlation coefficients, while important for model structure, don't specifically impact tail probability accuracy. Statistical variance of input parameters is important for model definition but doesn't determine the accuracy of tail probabilities in the output distribution. The fundamental principle of Monte Carlo simulation is that accuracy improves with more iterations, particularly for capturing rare events in the distribution tails.