Monte Carlo Simulation

Correct Answer: To focus sampling efforts on areas of high uncertainty while maintaining overall simulation accuracy

Adaptive Sampling Technique (AST) in Monte Carlo simulation is designed to intelligently focus sampling efforts on areas of high uncertainty while ensuring overall simulation accuracy. This is the correct approach because: It optimizes computational resources by concentrating on critical areas that need more detailed analysis while maintaining the statistical validity of the simulation. The other answers are incorrect because: - Analyzing variables based on historical mean values and standard deviations is a basic statistical approach that doesn't capture the adaptive nature of AST. - Generating samples that exclusively follow normal distribution patterns limits the simulation's ability to model real-world scenarios, which often follow different probability distributions. - Simply reducing computational time by limiting iterations to predetermined thresholds could compromise the accuracy of the simulation and doesn't reflect the intelligent sampling approach of AST.

Correct Answer: To measure how strongly two variables are related and move together

In Monte Carlo simulation for project schedule analysis, the correlation coefficient measures the strength and direction of relationships between different variables in the project schedule. This is crucial because project activities often have interdependencies. The correct answer is that it measures how strongly two variables are related and move together. This is essential for accurate simulation because many project activities are correlated - when one takes longer, related activities often take longer too. The other options are incorrect because: - Calculating standard deviation is a different statistical measure that shows variability, not relationships between variables - Determining probability distribution patterns relates to individual variables, not their relationships - Establishing mathematical relationships between input and output variables describes general modeling, not specifically what a correlation coefficient does in Monte Carlo simulation.

Correct Answer: The input variables have high variability and uncertainty in their values

In Monte Carlo simulation, a flattened curve with wide tails in the output data is a clear indication of high variability and uncertainty in the input variables. Here's why: A normal distribution typically shows a bell curve shape. When the curve becomes flattened and has wide tails, it means: - The input data has significant spread - There is high uncertainty in the estimates - Multiple variables likely have wide ranges of possible values - The project outcomes are less predictable Why other answers are incorrect: - Adding correlation coefficients would not address the fundamental issue of high variability - Base estimate refinement alone would not change the underlying uncertainty pattern - Advanced sampling techniques would not resolve the inherent variability in the input data The flattened curve with wide tails is a direct result of the input variables having high uncertainty and variability in their values, which is precisely what Monte Carlo simulation is designed to reveal.