PMI-RMP Certification Training - Latin Hypercube Stratified Sampling

The video tutorial explains the differences between Monte Carlo and Latin Hypercube methods, highlighting how Latin Hypercube achieves similar results with fewer iterations by using intervals. It then outlines guidelines for conducting quantitative risk analysis, including reviewing management plans, using historical data, and performing sensitivity analysis. The tutorial concludes with instructions on updating risk analysis reports and project documents, emphasizing the importance of tracking project risks and responses.

What is the primary difference between Monte Carlo analysis and Latin hypercube sampling?

• Monte Carlo requires more iterations for similar results.

• Latin hypercube provides random results.

• Latin hypercube is used for large-scale simulations.

• Monte Carlo uses fewer iterations than Latin hypercube.

How does Latin hypercube sampling ensure even distribution of results?

• By using intervals to cover the probability distribution.

• By focusing on a single outcome.

• By using a larger number of iterations.

• By employing random sampling techniques.

What is the first step in performing quantitative risk analysis?

• Conducting a project simulation.

• Performing a sensitivity analysis.

• Reviewing risk, cost, and schedule management plans.

• Updating the risk register.

Which technique is used to assess the impact of changes on project estimates?

• Historical information review

• Sensitivity analysis

• Probability distribution

• Monte Carlo simulation

What is updated to reflect project risk exposure after conducting risk analysis?

• Project management plan

• Risk analysis report

• Stakeholder register

• Cost management plan

What might a risk report include to indicate potential threats or opportunities?

• A list of completed tasks

• A prioritized list of individual project risks

• A summary of project milestones

• A detailed budget analysis

What forms the inputs to the plan risk responses process?

• Budget constraints

• Stakeholder feedback

• Quantitative risk analysis recommendations

• Project timeline