The course was a great way to link the theoretical statistical world with real world applications. Highly recommended!

R. Coates
President
Promana Project Strategies Inc.

I wanted to express my thanks and appreciation for the outstanding training you provided during the Quantitative Risk Analysis course held in Philadelphia. The course material was informative, challenging, and extremely well presented. It was an awesome learning experience for everyone. I have a great appreciation for Vose Consulting, because I honestly believe your organization is hands down the most knowledgeable risk modeling firm in the world. Thank you again.

Brian Watt
Chief Operating Officer / Chief Financial Officer
GECC, Inc

Introduction to Quantitative Risk Analysis is not just a class, it is an experience. There is a great depth and breadth of knowledge that Dr.Huybert Groenendaal brings to the table, not just in risk analysis, but in business and industry as a whole. The class brought to light and explained fully issues with risk analysis that I previously only suspected or wondered about. I would not hesitate to recommend the class to anybody with a true interest in risk analysis.

Participant Intro to Quantitative Risk Analysis course, 2006

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Who should attend

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Laptops

Participants are required to bring laptops loaded with Microsoft Word, Microsoft Powerpoint and Microsoft Excel. Trial copies of Crystal Ball and @RISK are available free of charge from the Decisioneering and Palisade web-site but these should not be installed too early as trial versions run out after 10 days.

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Teaching philosophy

This course aims to help participants understand rather than just learn the steps to do a risk analysis. This can only be achieved in a relaxed, informal and interactive environment using plenty of examples and hands-on exercises where students apply and adapt what they have learned.

We believe that:

When you hear something, you forget it.
When you see something, you remember it.
But not until you do something will you understand it

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ModelAssist

ModelAssist from Vose Consulting is a comprehensive risk analysis training and reference software tool. ModelAssist for Crystal Ball and ModelAssist for @RISK provide an in-depth explanation of many risk analysis concepts, techniques and methods and will therefore certainly greatly complements the modeling issues discussed during this course. It is particularly helpful as a reference for participants of the material that has been presented during the course.

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Course content

Day 1

Introduction to risk analysis

• Background of risk analysis and risk management
• Risk analysis as a team effort
• Going from data to knowledge to a useful decision tool
  • Dealing with the limits of current knowledge

Introduction to statistical descriptors

• Mean, mode, standard deviation, skewness, kurtosis, percentiles

Introduction to probability theory

• The use of distributions: uncertainty, variability and inter-individual variability
• Probability concepts
• Graphical representations of risk events: Venn diagrams, fault trees and event trees
• A look at some simple probability distributions

Introduction to risk modeling

• Monte Carlo simulation, Crystal Ball/@RISK and Excel
  • Brief tutorial on Crystal Ball/@RISK
• Calculation vs. simulation - the pros and cons of Monte Carlo
• Typical risk analysis results, their presentation and interpretation
• Practical problems to solve
• The most common probability distributions

Day 2

Stochastic processes - the basis of risk analysis

• Binomial Process
  • Binomial, beta, negative binomial and geometric distributions
  • Imperfect tests, machine failures, risk events, etc.;
• Poisson Process
  • Poisson, gamma, and exponential distributions
  • Modelling insurance claims, accidents, random outbreaks, etc.
• Hypergeometric process
  • Hypergeometric and inverse Hypergeometric distributions
  • Survey results, prevalence estimate with imperfect diagnostic test, gambling etc.
• Practical problems to solve

Day 3

Good practices in risk modelling

Common mistakes and how to prevent them

Introduction to analyzing and using data for risk analysis

• Statistical techniques
• Why we need uncertainty distributions not confidence intervals in risk analysis
• Creating uncertainty distributions with standard Classical Statistical tests
  • t-tests, z-tests, Chi-squared tests
  • Examples of estimation of population mean and standard deviation
• The Bootstrap to include uncertainty
• The use of Bayesian Statistics in risk analysis

Example risk analyses (a range of examples will also be covered during the course).

Wrap up and review of course material.

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