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| Fundamental Concepts | |
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| Exercises | |
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| Tutorials | |
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| Road Map | |
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| Overview of Decision Making with Insight and INSIGHT.xla 2.0 | |
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| Application Matrix | |
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| Analytical Modeling in Spreadsheets | |
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| Introduction | |
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| The Technology of Decision Making | |
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| Disciplined Intuition: A Philosophy | |
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| Analytical Models | |
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| Tutorial: Important Modeling Techniques | |
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| Understanding the Elements of a Worksheet Model | |
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| Separation of Data and Formulas | |
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| Making Sure the Model is Scalable | |
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| Experimenting with the Model | |
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| The Voices of Experience | |
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| The Pros and Cons of Spreadsheet Modeling | |
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| First the Cons | |
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| Now the Pros | |
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| The Building Blocks of Uncertainty: Random Variables | |
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| Introduction | |
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| From Manhattan Project to Wall Street | |
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| XLSim | |
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| Tutorial: Estimating Profit with Monte Carlo Simulation | |
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| An Example: Uncertain Profit | |
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| Monte Carlo Simulation: The Basic Steps | |
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| The Building Blocks of Uncertainty | |
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| Uncertain Numbers: Random Variables | |
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| Averages of Uncertain Numbers: Diversification and the Central Limit Theorem | |
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| Important Classes of Uncertain Numbers: Idealized Distributions | |
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| An Investment Example | |
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| Risk vs. Uncertainty: Risk Management | |
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| Value at Risk: Managing Risk in the Investment Example | |
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| Conclusion | |
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| The Buildings of Uncertainty: Functions of Random Variables | |
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| Introduction | |
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| Tutorial: Estimating Inventory Costs Given Uncertain Demand | |
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| An Inventory Problem | |
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| Simulating the Cost | |
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| Simulation Results | |
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| The Flaw of Averages | |
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| The Buildings of Uncertainty | |
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| Worksheet Models Based on Uncertain Numbers: Functions of Random Variables | |
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| Experimenting Under Uncertainty: Parameterized Simulation | |
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| The Increase of Option Prices with Uncertainty: Implied Volatility | |
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| Uncertain Numbers That Are Related to Each Other: Statistical Dependence | |
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| The Connection with Linear Regression | |
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| Portfolios of Correlated Investments | |
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| How Many Trials Are Enough? Convergence | |
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| Sensitivity Analysis: The Big Picture | |
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| Hypothesis Testing: Did it Happen by Chance | |
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| Conclusion | |
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| Uncertainties That Evolve Over Time | |
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| Introduction | |
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| Systems That Evolve Over Time | |
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| QUEUE.xla and Q_NET.xla | |
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| Simulation Through Time: Discrete-Event Simulation | |
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| A Fixed-Time-Incremented Simulation of a Forest Fire | |
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| Cellular Automata | |
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| Queuing Models | |
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| Classifying Queues | |
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| Fixed- versus Event-Incremented Time | |
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| Queuing Networks | |
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| The Extend Discrete Event Simulation Software | |
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| Combining Excel Models with Extend | |
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| Markov Chains | |
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| An Example: Market Share | |
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| MARKOV.xls | |
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| A Remarkable Property of Markov Chains | |
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| Modifying the Transition Matrix to Evaluate Replacement Strategy | |
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| Conclusion | |
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| Forecasting | |
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| Introduction | |
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| Causal Forecasting | |
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| Time Series Analysis | |
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| Using Excel's Regression and XLForecast | |
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| Tutorials: Regression and Time Series Analysis | |
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| Regression: Estimating Sales Based on Advertising Level | |
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| Time Series Analysis: Predicting Future Sales Based on Past History | |
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| The Importance of Errors | |
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| Errors Generated by Regression | |
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| Errors Generated by Time Series | |
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| Predicting the Past | |
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| Conclusions | |
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| Explanation of Regression and Exponential Smoothing | |
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| Regression | |
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| Exponential Smoothing | |
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| Decision Trees | |
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| Introduction | |
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| An Example: Ice Cream and Parking Tickets | |
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| Good Decisions versus Good Outcomes | |
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| XLTree | |
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| Tutorial: Building a Decision Tree | |
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| Experimental Drug Development | |
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| Building a Decision Tree with XLTree | |
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| Decision Analysis: Basic Concepts | |
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| Utility | |
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| Probability | |
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| Expected Value | |
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| Decision Forks | |
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| Uncertainty Forks | |
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| Sensitivity Analysis | |
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| Conditional Probability | |
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| The Value of Information | |
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| State Variables | |
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| Mustering the Courage of Your Convictions | |
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| Overview of Optimization | |
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| Introduction | |
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| The ABC's of Optimization | |
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| Tutorial: Maximum Profit | |
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| How Many Boats to Produce? | |
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| The ABC's of Optimization | |
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| Interacting with the Model: What's Best! | |
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| The D's of Optimization: Dual Values | |
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| Basic Optimization Examples | |
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| Product Mix | |
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| Blending | |
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| Staff Scheduling | |
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| Transportation | |
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| Network Flow Models | |
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| Conclusion | |
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| Extensions of Optimization | |
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| Extending the Application of Optimization | |
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| Integer Variables | |
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| Combining Optimization Models: An Object Oriented Approach | |
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| Optimization Under Uncertainty | |
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| Nonlinear Optimization | |
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| Combinatorial Optimization | |
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| Complete Evaluation Times for N-City Traveling Salesman Problem | |
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| Common Errors in Optimization Models | |
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| Linear and Nonlinear Formulas | |
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| Improper Constraints | |
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| Local Maxima or Minima in Nonlinear Optimization | |
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| The Basics of Optimization Theory | |
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| Optimizing a Simplified BOAT Problem | |
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| Linear versus Nonlinear Problems | |
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| More on Dual Values | |
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| Conclusion | |
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| Queuing Equations: QUEUE.xla and Q_NET.xla | |
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| Two-Parameter Exponential Smoothing for Estimating Trends | |
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| Software Command Reference | |
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| XLSim | |
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| QUEUE.xla and Q_NET.xla | |
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| Extend | |
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| XLForecast | |
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| XLTree | |
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| Optimization Software | |
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| References | |
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| Index | |
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| Software Contained on the CD ROM | |
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| Praise for the 1st Edition | |