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Course Descriptions

Course 1 - Modelling with @RISK

This hands-on course is designed for anyone who quantitatively models aspects of their business, or performs risk or decision analysis as part of their work, and the lessons apply to all industries and modelling requirements. While mainly using simple example models to help explain the software and techniques, the knowledge gained can be applied to any application involving uncertainty in the Excel environment.

Attendees will be introduced to the concepts and methods necessary to simulate almost all potential future outcomes, develop a risk assessment and to make defensible decisions under uncertainty. Discover how to translate deterministic Excel analysis into an @RISK model that uses Monte Carlo simulation to quantify exposure to risk and test mitigation strategies. The importance of interdependent relationships among variables is discussed, as well as how to implement these relationships in spreadsheet models. Business and modelling decisions will be compared and optimised using special @RISK functions and charts. Several key modelling concepts will be introduced, as well as multiple reporting options. Fitting distributions and time series models to historical data will be explored, as well as many other advanced features of @RISK. Examples will be presented and created which will demonstrate how to effectively use the software and interpret the results.

Prerequisites:

  • Some experience building models with Excel
    Minimum requirements include knowledge and experience with the following:
    • Opening, saving and closing Excel files
    • Entering data and creating formulas
    • Copying formulas, using relative and absolute referencing, range names
    • Formatting cells
  • Strongly Recommended - Review the @RISK Getting Started Guide, and the Getting Started examples in @RISK
  • Recommended - some familiarity with basic statistics

Course Agenda:


Day 1 - Model Building and Simulation Results
  • Introduction to Monte Carlo Simulation – Exploring @RISK
  • How to Choose Continuous Distributions – Pert, Lognormal, Normal, Uniform, Triangle etc.
  • Alternate Parameter Distributions – Estimating Parameters
  • Using Discrete Distributions – Bernoulli, Binomial, Poisson, RiskDiscrete etc.
  • Output and Results interpretation: Histograms, Cumulative Graphs
  • Sensitivity Analysis Using @RISK – Tornado Charts, Spider Graphs

Day 2 – Simulation Options, Reporting and Advanced Modelling
  • Exploring the Simulation Settings – Convergence, Reproducibility of Results
  • Reporting Simulation Results in Excel – Standard Reports, User-Defined Results
  • Using Historical Data to Define Distributions – Goodness of Fit Metrics, Bootstrapping, Individual and Batch Fit
  • Customised Distributions – Distribution Artist, RiskGeneral, RiskCumul, RiskVary, RiskSplice, RiskMakeInput
  • Modelling Risks and Shocks in a System – RiskCompound

Day 3 – Decision Optimisation and Advanced Concepts
  • Decision/Assumption Comparison in @RISK – RiskSimtable
  • Optimisation Under Uncertainty - RISKOptimizer
  • Correlating Input Variables – Matrixes and Copulas
  • Using Historical Data to Define Time Series Models – Individual and Batch Fit
  • Advanced Analyses – Goal Seek, Stress Analysis, Advanced Sensitivity Analysis
  • XDK – Using VBA for Excel to run @RISK Procedures


Course 2 - Project Cost and Risk Register Modelling with @RISK

This hands-on course is designed for anyone who performs risk or decision analysis as part of their work, and applies to all industries and modelling requirements in the field of Projects and Project Management. Using common examples such as project cost models and simple risk registers to help explain the software and techniques, the knowledge gained can be applied to any project application involving uncertainty in the MS Excel environment.

Attendees will be introduced to the concepts and methods necessary to develop a risk assessment and to make a defensible decision under uncertainty using Palisade Corporation software. Discover how to translate a deterministic Excel analysis into an @RISK model that uses Monte Carlo simulation to quantify exposure to risk. A Risk Register will be created with the focus on how to build a technically correct model that provides useful information to project managers and decision makers. The importance of interdependent relationships among variables is discussed, as well as how to implement these relationships in spreadsheet models. Several key modelling concepts will be introduced, including the creation of report templates. Key considerations when combining distinct model aspects, or multiple projects into a program/portfolio will also be addressed. Examples will be presented and built which will demonstrate how to effectively use the software and interpret the results.

Prerequisites:

  • 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 formulas
    • Copying formulas, using relative and absolute referencing, range names
    • Formatting cells
  • Strongly Recommended - Review the @RISK Getting Started Guide, and the Getting Started examples in @RISK
  • Recommended - some familiarity with basic statistics

Course Agenda:


Day 1 – Model Building and Simulation Results
  • Introduction to Monte Carlo Simulation – Exploring @RISK
  • How to Choose Continuous Distributions – Pert, Lognormal, Normal, Uniform, Triangle etc.
  • Alternate Parameter Distributions – Estimating Parameters
  • Using Discrete Distributions – Bernoulli, Binomial, Poisson, RiskDiscrete etc.
  • Customised Distributions – Distribution Artist, RiskGeneral, RiskCumul, RiskVary, RiskSplice, RiskMakeInput
  • Output and Results interpretation: Histograms, Cumulative Graphs

Day 2 – Simulation Options, Reporting and Risk Registers
  • Sensitivity Analysis Using @RISK – Tornado Charts, Spider Graphs
  • Exploring the Simulation Settings – Convergence, Reproducibility of Results
  • Reporting Simulation Results in Excel – Standard Reports, User-Defined Results
  • Risk Register modelling – Avoiding Common Mistakes, RiskCompound

Day 3 – Contingency and Advanced Modelling Concepts
  • Advanced Analyses – Goal Seek, Stress Analysis, Advanced Sensitivity Analysis
  • Correlating Input Variables – Matrixes and Copulas
  • Decision/Assumption Comparison in @RISK – RiskSimtable
  • Combining Cost, Risk and Opportunity models – Appropriate Frameworks
  • Contingency and Management Reserve – Key Concepts, Communication
  • Portfolio Level Modelling – Combining Models