Who should attend

Anyone in business, government and science with an interest in quantitative risk analysis such as professionals needing to perform quantitative risk analysis in epidemiology, finance or operations, engineering, project risk analysis and researchers who are involved in risk analyses. Also, people who have experience in risk analysis using spreadsheets but wants to learn how to use a more sophisticated modeling environment such as R.

Previous experience using R is not required, but participants are strongly encouraged to read the R introductory manual or a briefer version such as this.

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Laptops

Participants are required to bring laptops loaded with R, and Microsoft Powerpoint. R is an open-source freeware and can be downloaded free of charge from the R Project website. As R is updated constantly, please download the latest version before attending the course. Also, it is recommended participants use a text editor such as Tinn-R (Windows) to facilitate the display and storage of their code.

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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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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 in the context of risk analysis

• 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

Risk modeling in R

• Data structures used in simulation modeling
• Basic data manipulation and exploration
• Probability distributions in R and their differences with other software

Day 2

Risk modeling in R (continued)

• Using loops and vectorized calculations for simulation
• Storing and retrieving simulation results
• Graphical exploration of simulation data
• Basic simulation analyses and diagnostics

Basics of risk modeling

• Monte Carlo simulation
• 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 3

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 4

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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