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Intermediary risk analysis

Duration: 1 week

Course overview

This course is designed for those people who already have some experience in risk analysis modelling with the Crystal Ball/Excel or @RISK/Excel combination but wish to extend that knowledge.

The course provides an in-depth explanation of various concepts and techniques necessary for superior level risk assessments. Prior knowledge of the material covered in the Basic Risk Analysis course or equivalent, together with significant experience in risk modelling, will be needed.

The course is designed to encourage the modeller to develop creative problem solving skills to ensure that their analyses are decision-focused, accurately, efficient and the results presented in a manner that provides decision makers with the clearest and most helpful input.

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Training material

All lecture notes are provided as PowerPoint files. A CD of these files is provided to each participant. Printed handouts are also provided. The CD also contains all model files produced for the course. Any extra models developed during the course are downloadable from a private page on this web site dedicated to the course.

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Course format

The course runs from 09:00 to 17:00 each day. Morning and afternoon coffee, and lunch are provided. Optional evening workshops on the first and second days allow extra time for running through example models and exercises.

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Who should attend

The course is tailored to the needs of people in business, science and government who apply risk analysis to their work and wish to extend their capabilities.

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Prerequisites

All models are developed using Excel and Crystal Ball or @RISK. It is essential that all participants are proficient in Excel and Crystal Ball or @RISK. It is not expected that the features of these programs will need to be explained during the course.

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Laptops

Participants are required to bring laptops loaded with Microsoft Word, Microsoft PowerPoint and Microsoft Excel and Decisioneering's Crystal Ball 7 or Palisade's @RISK 4.5 Professional installed, and with a CD drive. Trial copies of Crystal Ball and @RISK are available free of charge from Decisioneering and Palisade web-sites but these should not be installed too early as trial versions run out after 7 days for Crystal Ball and 10 days for @RISK.

Participants intending to take Module 2 only should ensure that they have already taken a course equivalent to Module 1 or possess the equivalent knowledge.

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ModelAssist

ModelAssist from Vose Consulting is a comprehensive risk analysis training and reference software tool. ModelAssist provides an in-depth explanation of all of the risk analysis concepts, techniques and methods introduced in the course and greatly complements the course material. It is particularly helpful to let participants learn the material that has been presented during the course at their own pace.

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Teaching philosophy

All of Vose Consulting's courses aim to help the participants understand (rather than 'learn') risk analysis, which can only be achieved through a relaxed, informal and interactive environment, through plenty of examples and hands-on exercises where students apply and adapt what they have learned.

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Social event

Part of the value of specialist courses like these is the contacts one makes with others in the same field. It is quite a long course, and we'll need a break from time to time. The entertainment we provide is an excellent opportunity to relax, have some fun, build a rapport with other participants, establish some contacts and sample some of the local culture.

We therefore arrange optional fun, interactive social events which take advantage of local attractions, cuisine and culture. All social events are included in the course fee.

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Course content

Day 1

  • Introduction
  • Uncertainty, variability and inter-individual variability
    • Examples of modelling problems where uncertainty and variability are separated
    • Structures of two-dimensional (second order) risk analysis models
  • Binomial Process
    • Binomial, beta, negative binomial and geometric distributions.
    • Problems to solve
    • Nested binomials
  • Poisson process
    • Poisson, gamma, m-Erlang and exponential distributions.
    • Mixed Poisson and binomial processes

Day 2

  • Poisson process (cont)
    • Problems to solve
    • Renewal process and its modelling
  • Hypergeometric process
    • Hypergeometric and inverse hypergeometric distributions
    • Problems to solve
  • Central Limit Theorem
    • Normal and lognormal

Day 3

  • Classical statistics
    • Estimation of population mean and standard deviation
    • Estimation of population prevalence and Poisson mean
  • Bayesian Inference
    • Theory and derivation
    • Simple examples
    • More involved examples (e.g. multi-parameter problems)
    • Problems to solve

Day 4

  • The Bootstrap
    • Applications and problems to solve
  • Determining distributions from data
    • First order distribution fitting
    • Fitting to parametric and non-parametric distributions
    • MLE and goodness of fit statistics
    • Using linear solvers with gof statistics for best fit
    • Second order distribution fitting
    • Parametric and non-parametric distributions
    • Likelihood estimating, Bootstrapping, other methods
    • Problems to solve

Day 5

  • Time series modelling
  • More advanced modelling techniques
  • Looking at participants modelling issue

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