This is paired with the Introduction of FDA iRisk® and the combined courses are held over one week. The course will cover basic modeling concepts, including both deterministic and probabilistic modeling approaches. Participants will be taught how to build risk assessment models using Excel with one of the more commonly-used commercial software packages (@RISK). This course will provide participants with a strong foundation in stochastic processes, probabilistic risk assessment and Monte Carlo simulation. Participants will gain a deeper understanding of the principles and mechanics of Monte Carlo simulation, build models using these principles, and learn how to analyze probabilistic models in a risk assessment context. The course will also discuss how to use data and expert opinion when building models. Participants can expect to gain hands-on experience in building and analyzing computer-based probabilistic models and experience some techniques and challenges to expect in presenting their results to various audiences. Learning by example, participants will be given exercises involving elements of real world risk assessments that are being used in current policy and risk management.

Prerequisite: None

It is strongly recommended that this course be taken after you have completed the Risk Management and Qualitative Risk Assessment course. These courses provide contextual information about risk analysis that is not repeated here. Participants should also have basic knowledge of probability and statistics and intermediate level skills in using Microsoft Excel 2003.

Resources

Excel: There are web-based resources that provide introductory Excel 2003 training. Many such courses are available - some at no cost - like the one found at www.videoprofessor.com.
Basic Statistics: The quantitative methods course does not require in-depth knowledge of statistics, but an understanding of basic terminology is necessary. There are web-based resources that provide information about basic theory in probability and statistics. Some examples include http://www.robertniles.com/stats and http://www.statsoft.com/textbook.

Overview of Topics

Going beyond terminology, Introduction to modeling, Deterministic modeling, Probability, Monte Carlo simulations, Triangular, Pert and Beta distribution and Bayes’ Theorem
Learning Objectives

• Understand why models are useful
• Understand important tradeoffs in the design of models
• Understand the differences between deterministic and stochastic models
• Gain a strong foundation in basic probability theory and probability distributions
• Be able to build basic probabilistic models using Excel and @RISK
• Simulation principles and techniques
• Stochastic processes
• Scenario and Sensitivity analysis