Quantitative Risk Analysis with R - (4 days)

Duration: 4 full days
Minimum of 6 people and maximum of 15 people

Course overview

This 4-day course will cover the core principles of quantitative risk analysis and the most important risk modeling principles, methods and techniques. The course will be taught using the R statistical language but the lessons apply equally well to other modeling environments. The focus of the course is on how to conduct accurate and effective quantitative risk analyses, including best practices of risk modeling, selecting the appropriate distribution, using data and expert opinion, and avoiding common mistakes. The course will also cover essential probability and statistics theory and various stochastic processes to provide the participants with a solid understanding of quantitative risk analysis

Who should attend

Anyone in business, government and science with an interest in quantitative risk analysis such as professionals needing to perform (or provide feedback) quantitative risk analysis in finance or operations, epidemiology, engineering, project risk analysis, among others. Also, people who have experience in risk analysis using spreadsheets but want to learn how to use a more flexible modeling environment such as R.


Participants are required to bring laptops loaded with R, and a pdf reader. R is an open-source freeware that works for Windows, Mac, or Linux 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 that participants use a code editor to facilitate the writing and storage of code: we recommend Tinn-RTinn-R for Windows users, and RStudio for Mac users.

Prior experience using R or other simulation tools is not required

Teaching philosophy

All of EpiX Analytics' courses aim to help the participants understand risk analysis from the bottom-up, which is achieved through a relaxed, informal and interactive environment using plenty of examples and hands-on exercises where students apply and adapt what they have learned.

Free ModelAssist

ModelAssist is a comprehensive risk analysis training reference and is free of cost. This reference tool provides an in-depth explanation of all of the risk analysis concepts, techniques and methods introduced in this course and greatly complements the course material. ModelAssist can be downloaded directly from our website here.   

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
    • Modeling 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.

Registration Discounts

Early registration - 10% off registration (register and pay 30 days before course start date)
* Government/Academic - 5% off registration (Proof of eligibility requested after registration)
* Student - 25% off registration (Proof of eligibility requested after registration)
* Please contact us to obtain the discount code that you qualify for.
3 registrants - 17% off total registration
4 registrants - 25% off total registration
5+ registrants - please contact us for special discount rate

Please note that discount rates cannot be combined. Discounts exclude online courses via statistics.com and joint courses offered with other institutions.

Please contact us to inquire about courses not currently scheduled.

Registration details

Start Date December 04, 2017
End Date December 07, 2017
Individual Price $2,495
Fort Collins, CO, USA
117 E Mountain Ave #225, Fort Collins, CO 80524, USA
Fort Collins, CO, USA

Group Rate

#Registrants Rate/Person($)
3 2,071
5 1,871


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Brief History of EpiX Analytics

EpiX Analytics was founded in 2003 in Princeton, NJ under the Vose Consulting name, and among its offerings it included specialized software to perform Monte Carlo simulations. Read More >>