Part II of our DecisionTools Suite Live Web training consists of two 4-hour sessions spread over 2 days. Materials presented in Part II build upon those presented in Part I. Therefore, it is highly recommended that registrants have completed the Part I course or demonstrate a strong working knowledge and experience with the DecisionTools Suite.

Refining the model:
Correlation and Interdependence

1. Correlating Risk Inputs
1. Impact
2. Simple Calculation Example
2. Correlation Methodology
1. Pearson
2. Rank
3. Application
3. Time Dependent Risk
1. Impact
2. Application
3. Time Series Correlation

Selecting Distributions

1. Introduction
2. Expanded Distribution Selection
1. Bootstrap Method
3. Distribution Fitting
1. Interface
2. Fit Manager
3. Linking Data
4. Expert Opinion
1. Alternate Parameters
2. Judgment and Bias
5. @RISK Library

Building a Decision Tree Using PrecisionTree

1. Laying out options for the case study model
2. Simple analysis of outcomes
1. Interpreting tree results
1. Calculation method
2. Value assessment
2. Risk Profile
1. Statistics
3. Policy Suggestion
3. Sensitivity Analysis
1. One-Way
2. Two-Way
4. Assessment

Building a Scenario Analysis Model
Using TopRank

1. Structuring the Excel model
2. Defining What-If parameters
1. Analysis Settings
2. Adding Outputs
3. What-If Sensitivity Analysis
1. Reports
2. Detail
4. Assessment

Evolving An Optimal Solution
Using RISKOptimizer

1. Introduction to optimization
1. Genetic algorithm
2. Adjustable cells
3. Variables
4. Constraints
5. Solving methods
6. Reports and solution
2. Application to case study
3. Assessing variation of optimal solution
1. Applying @RISK to optimized results
2. Analysis of simulation
3. Reporting capabilities revisited

Additional Features

1. Multiple simulations (@RISK)
1. Simtable
2. Scenarios
2. Advanced Analyses (@RISK)
1. Stress
2. Advanced Sensitivity
3. Other Solving methods (RISKOptimizer)
4. Influence Diagrams (PrecisionTree)
5. Simulating decision trees (PrecisionTree & @RISK)

Statistical Analysis

1. Statistical Analysis interface (StatTools)
1. Data Analysis
2. Reporting capabilities
3. Highlight basic statistical functions

Predicting Outcomes
Using NeuralTools

1. Defining a model
2. Train
3. Test
4. Predict

Review of Additional Example Models