The 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. They will also learn how to use other Palisade products, including RISKOptimizer and PrecisionTree, to help to optimize decisions under uncertainty. 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
   

An introduction to risk analysis using @RISK, including the selection of defensible distributions, and a range of practical modelling situations.

1. Introduction to risk analysis
2. Introduction to @RISK using simple examples
3. Selection of distributions, including use of RISKview
4. Learning to use @RISK and its features: Further examples and features
   

Using @RISK in more advanced modelling situations, including fitting distributions to data, capturing interdependence between uncertain inputs in an @RISK model, and modelling uncertainty over time. Introduction to other Palisade products for risk analysis, including PrecisionTree for decision tree analysis, TopRank for sensitivity analysis, and RISKOptimizer for making optimal decisions under uncertainty.

1. Use of BestFit
2. Building interdependence between uncertain inputs through correlation
3. Capturing uncertainty over time: Introduction and hands-on modelling
4. Additional modelling examples and concepts
5. Introduction to optimisation under uncertainty: Using RISKOptimizer
6. Building decision trees using PrecisionTree
7. Introduction to TopRank

Example models will be used to demonstrate specific points about the use of the software and the background behind these concepts.

The 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. They will also learn how to use other Palisade products, including RISKOptimizer, PrecisionTree and TopRank, to help to optimize decisions under uncertainty. Examples will be presented which will demonstrate how to effectively use the software and to interpret the results in a wide range of financial and non-financial applications.

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

Prerequisites:

1. Some experience of building models with Excel
2. Recommended—completion of applicable software tutorials on the Palisade web site

An introduction to risk analysis using @RISK, including selection of defensible distributions, and a range of practical modelling situations.

1. Introduction to risk analysis
2. Introduction to @RISK using a range of simple examples
3. Selection of distributions, including use of RiskView,
4. Learning to use @RISK and its features: Working through examples

Example models used include profit analysis of a multi-product business, a risk analysis of a client-supplier contract, the simulation of a bidding situation, the aggregation of multiple risks into a single measure, the modelling of food toxin levels, and of earthquake damages.

Using @RISK in more advanced modelling situations, including fitting distributions to data, capturing interdependence between uncertain inputs, and modelling uncertainty over time. Introduction to other Palisade products for risk analysis, including PrecisionTree for decision tree analysis, TopRank, and RiskOptimizer for making optimal decisions under uncertainty.

1. Further aspects of selecting and using probability distributions, including the use of BestFit, of expert judgment, of distributions for parameter estimation and model calibration, and the use of Bootstrapping sampling methods.
2. Creating interdependence between uncertain inputs through correlation and other dependency relationships
3. Capturing uncertainty over time: Hands-on modelling of a variety of time series models
4. Introduction to optimisation under uncertainty: Using RISKOptimizer
5. Building decision trees using PrecisionTree, and introduction to TopRank.

Examples used include analysis of stock market data, cash flow models and NPV analysis, options valuation, and portfolio management. Non-financial applications include models of animal growth, epidemiology, drug testing, and oil drilling.

The course is designed to introduce attendees to the concepts and methods necessary to predictive models using the popular method of Neural Networks along with the popular NeuralTools software from Palisade Corporation. Attendees will obtain an insightful understanding of prediction with neural networks and feel comfortable constructing and interpreting prediction models and results to accommodate decision making. This course is intended to predominantly work within the NeuralTools environment, though there will be some reference to Palisade Corporation’s StatTools software.

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

Seminar Outline:

1. Introduction to Predictive modelling
• Common methods and applications
2. Introduction to Neural Networks
• What are neural networks?
• Using neural networks for predictive modelling
3. NeuralTools
• Exploring the tool
• Training, testing and prediction with neural networks
4. Predictive Models
• Categorical and numerical models
• Analysing and interpreting results
• Maximising the value and integrity of your models by incorporating other Palisade tools

Advanced Risk Assessment will provide experienced modelers and @RISK users with the skills to develop complex, robust, and defensible risk analysis models. The course is designed to examine in detail the three fundamental components necessary for producing defensible risk assessments using Palisade software:

1. Selecting a Defensible Distribution
2. Defining Defensible Time Dependent Components
3. Defining Interdependence Between Uncertain Variables: Correlation.

In addition, a variety of techniques for modelling a risk analysis will be presented. These techniques will be examined through example spreadsheet models. Some of these techniques include:

1. Modeling conditional events
2. Modeling risk preferences
3. Stochastic dominance
4. Introduction to automating @RISK using @RISK macros and VBA
5. Incorporation of decision trees and optimisation into simulation modelling using RISKOptimizer and PrecisionTree in conjunction with @RISK.

Throughout the course, examples will be presented to the students as class exercises. These examples will demonstrate the various modelling capabilities of the software and show how to interpret the results.

Upon completion of this course, students will have learned advanced concepts and techniques used to perform risk analyses using Palisade software.

Prerequisites:

1. Able to utilize basic Excel functions
2. Recommended
   a. Completion of Intro to Risk Assessment Course
   b. Experience developing models in Excel
   c. @RISK experience
   
   

1. Review of risk analysis using @RISK
2. Developing an @RISK model step-by-step
   a. Modeling techniques: Modeling conditional events
   b. Automating @RISK with VBA Macros
   c. Exercises
3. Modeling a Key Component of a Risk Analysis: Correlation
   a. Building interdependence between uncertain inputs
   b. Model structure vs. @RISK correlation
   c. Exercises

Selecting defensible distributions; modelling uncertainty over time (time series uncertainty)

1. Selecting a defensible distribution
   a. Based on industry conventions
   b. Based on data
   c. Based on expert judgment
   d. Exercises
2. Capturing uncertainty over time, or time series uncertainty:
   a. Ad Hoc Methods
   b. Regression
   c. Exponential Smoothing (optional)
   d. Box Jenkins (optional)
   e. Exercises
3. Advanced optimisation using RISKOptimizer
4. Advanced decision tree modelling using PrecisionTree
5. Enhanced sensitivity analysis using TopRank

1. Overview of @RISK models: reserves, cashflow, production forecasts, prices, Capex, and Opex
2. The language of statistics and types of distributions, aided by BestFit and RISKview
3. Using @RISK for simple models: inputs, outputs, settings, simulation, graphs, reports
4. Monte Carlo Simulation, illustrated with applications

More advanced applications of @RISK for those with some experience with @RISK and other DecisionTools software, including:

1. Choosing the right distribution
2. Working with experts
3. Changing distributions
4. Modeling correlation
5. Specific models for cost and time
6. Production forecasts and economics

1. @RISK models: Hands-on model building covering topics of interest to the seminar such as:
   1. Handling rare events
   2. Modeling dependent projects
   3. Linking component parts of a larger model
   4. Comprehensive field development model
2. PrecisionTree models: Hands-on modelling covering topics of interest to the seminar such as:
   1. Performing sensitivity analysis with decision trees
   2. Estimating the value of additional information
3. Introduction to RISKOptimizer
1. Combining classic optimisation with Monte Carlo simulation
2. Examples

Project Risk Assessment using @RISK for Project is designed to introduce project managers to the concepts and methods necessary for developing a defensible risk analysis. The software used is @RISK for Project and Microsoft Project. Students will discover how to translate known information about a project’s risk into a model that can be used to quantify exposure and test mitigation strategies. Throughout the course, several examples will be presented to the students as class exercises. These examples will demonstrate various modelling techniques, as well as the interpretation of the risk analysis results.

Palisade Corporation is a PMI Registered Education Provider

Project Management Professionals attending this course can earn 15 PMI Professional Development Units.

Upon completion of this course, students will have been exposed to a wide range of modelling methods and will have the necessary prerequisites for performing a risk analysis on schedules using Microsoft Project and @RISK for Project.

Materials presented on Day 2 build upon those presented on Day 1. Therefore, registrations are accepted for both days only.

1. Introduction to Risk Analysis
2. Getting Started with @RISK for Project
3. Selecting and Defining Probability Distributions
4. Interpretation of Risk Analysis Results
5. Introduction to @RISK Modeling Techniques

1. Correlated Random Variables
2. Developing and Testing Risk Mitigation Strategies
3. Case Example - Class Project
4. Advanced @RISK Modeling Techniques

Course Objectives: The course is designed to introduce attendees to the concepts and methods used in the valuation of options and real options. Such analyses are being performed ever more frequently, both in the context of the financial markets and for more general management decision purposes. The course will cover the fundamentals of the theory and practice of options and derivatives valuation. Models will be built using both Excel and Palisade’s @RISK software.

1. Some experience of building models with Excel
2. Previous exposure to probability theory
3. Ideally: Completion of Palisade Risk Assessment training seminar (as a minimum: completion of @RISK software tutorial on the Palisade web site)
   
   

• Introduction to real options
• Introductory example
• Uses of real options analysis
• Core concepts and definitions
• Brief recap on using @RISK to model risk
• Model building and further examples
• Formulating the situation and building the models
• Examples of real options
• Introduction to options and risk-neutral valuation
• Similarities and differences between real options and options
• Asset price random walks
• Risk-neutral valuation using the binomial method
• Examples
• Further topics
• Black-Scholes equations and formulae
• Introduction to option exercise, in theory and in practice
• Linking risk-neutral valuation and traditional discounting
• Q&A, discussion, close