Decision-Making and Quantitative Risk Analysis using @RISK 5.0 (8023/AACEI Two)

This hands-on course is designed to introduce attendees to the concepts and methods necessary to develop a risk assessment and to make a defensible decision under uncertainty, using Palisade Corporation software. Attendees will discover how to translate their deterministic Excel analysis into an @RISK model that can be used to quantify exposure and test mitigation strategies. The importance of correlation is discussed, as well as how to include it in spreadsheet models. Business and modelling decisions will be compared using special @RISK functions and charts. Several key modelling concepts will be introduced, as well as creating report templates. Examples will be presented which will demonstrate how to effectively use the software and to interpret the results.

Please note that this course does not cover @RISK for Project.

Prerequisites:
   1. Some experience of building models with Excel
      Minimum requirements include knowledge and experience of the following:
          * Opening, saving and closing Excel files
          * Entering data and creating formula
          * Copying formulae, using relative and absolute referencing, range names
          * Formatting cells
          * Creation of basic graphics (XY scatter plots and line graphs)
   2. Recommended—completion of applicable software tutorials on the Palisade web site
   3. Recommended—some familiarity with basic statistics

Day 1:

An introduction to risk analysis using @RISK 5.0, including adding uncertainty to models and interpreting output.

  1. Introduction to Probability, Monte Carlo Simulation and Statistics
  2. Exploring the @RISK environment
  3. Adding uncertainty to deterministic models
  4. Simulating and simulation settings
  5. Output and results interpretation
  6. Special @RISK functions
  7. Choosing the ‘right’ distribution

Day 2:

Extends on the concepts of the first day, including making defensible distribution selections, correlation and advanced reporting.

  1. Discrete Distributions
  2. Model logic
  3. Fitting distributions to data
  4. Justifying expert opinion
  5. Correlation – its importance and implementation
  6. Policy comparison
  7. Report Templates


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