Decision Making with Insight

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1. ANALYTICAL MODELING IN SPREADSHEETS. Introduction. The Technology of Decision Making. Disciplined Intuition: A Philosophy. Analytical Models. Tutorial: Important Modeling Techniques. Understanding the Elements of a Worksheet Model. Separation of Data and Formulas. Making Sure the Model is Scalable. Experimenting with the Model. The Voices of Experience. The Pros and Cons of Spreadsheet Modeling. First the Cons. Now the Pros. 2. THE BUILDING BLOCKS OF UNCERTAINTY: RANDOM VARIABLES. Introduction. From Manhattan Project to Wall Street. XLSim(R). Tutorial:Estimating Profit with Monte Carlo Simulation. An Example: Uncertain Profit. Monte Carlo Simulation: The Basic Steps. The Building Blocks of Uncertainty. Uncertain Numbers: Random Variables. Averages of Uncertain Numbers: Diversification and the Central Limit Theorem. Important Classes of Uncertain Numbers: An Investment Example. Risk vs. Uncertainty: Risk Management. Value at Risk: Managing Risk in the Investment Example. Conclusion. 3. THE BUILDINGS OF UNCERTAINTY: FUNCTIONS OF RANDOM VARIABLES. Introduction. Tutorial: Estimating Inventory Costs Given Uncertain Demand. An Inventory Problem. Simulating the Cost. Simulating Results. The Flaw of Averages. The Buildings of Uncertainty. Worksheet Models Based on Uncertain Numbers: Functions of Random Variables. Experimenting Under Uncertainty: Parameterized Simulation. The Increase of Option Prices with Uncertainty: Implied Volatility. Uncertain Numbers That Are Related to Each Other: Statistical Dependence. The Connection with Linear Regression. Portfolios of Correlated Investments. How Many Trials Are Enough? Convergence. Sensitivity Analysis: The Big Picture. Hypothesis Testing: Did it Happen by Chance. Conclusion. 4. UNCERTAINTIES THAT EVOLVE OVER TIME. Introduction. Systems That Evolve Over Time. QUEUE.xla and Q_NET.xla. Simulation Through Time: Discrete-Event Simulation. A Fixed-Time-Incremented Simluation of a Forest Fire. Cellular Automata. Queuing Models. Classifying Queues. Fixed- versus Event-Incremented Time. Queuing Networks. The Extend(TM) Discrete Event Simluation Software. Combining Excel Models with Extend. Markov Chains. An Example: Market Share. MARKOV.xls. A Remarkable Property of Markov Chains. Modifying the Transition Matrix to Evaluate Replacement Strategy. Conclusion. 5. FORECASTING. Introduction. Causal Forecasting. Time Series Analysis. Using Excels Regression and XLForecast. Tutorials: Regression and Time Series Analysis. Regression: Estimating Sales Based on Advertising Level. Time Series Analysis: Predicting Future Sales Based on Past History. The Importance of Errors. Errors Generated by Regression. Errors Generated by Time Series. Predicting the Past. Conclusions. Explanation of Regression and Exponential Smoothing. Regression. Exponential Smoothing. 6. DECISION TREES. Introduction. An Example: Ice Cream and Parking Tickets. Good Decisions versus Good Outcomes. XLTree. Tutorial: Building a Decision Tree. Experimental Drug Development. Building a Decision Tree with XLTree. Decision Analysis: Basic Concepts. Utility. Probability. Expected Value. Decision Forks. Uncertainty Forks. Sensitivity Analysis. Conditional Property. The Value of Information. State Variables. Mustering the Courage of Your Convictions. 7. OVERVIEW OF OPTIMIZATION. Introduction. The ABCs of Optimization. Tutorial: Maximum Profit. How Many Boats to Produce? The ABCs of Optimization. Interacting with the Model: WhatsBest! The Ds of Optimization: Dual Values. Basic Optimization Examples. Product Mix. Blending. Staff Scheduling. Transportation. Network Flow Models. Conclusion. 8. EXTENSIONS OF OPTIMIZATION. Extending the Value of Optimization. Integer Variables. Combining Optimization Models: An Object Oriented Approach. Optimization Under Uncertainty. Nonlinear Optimization. Combinatorial Optimization. Complete Evaluation Times for N-City Traveling Salesman Problem. Common Errors in Optimization Models. Linear and Nonlinear Formulas. Improper Constraints. Local Maxima or Minima in Nonlinear Optimization. The Basics of Optimization Theory. Optimizing a Simplified BOAT Problem. Linear versus Nonlinear Problems. More on Dual Values. Conclusion. Appendix A: Queuing Equations: QUEUE.xla and Q_NET.xla. Appendix B: Software Command Reference. XLSim(R). QUEUE.xla and Q_NET.xla. Extend(TM). XLForecast(TM). XLTree(TM). Optimization Software. References. Index. Software Contained on the CD-ROM.

Dr. Savage received his Ph.D. in computer science from Yale University in 1973. He spent a year at General Motors Research Laboratory, then joined the faculty of the University of Chicago Graduate School of Business, where he taught regular classes until 1990. He then moved to Stanford University where he currently teaches courses on Analytical Modeling in Spreadsheets and directs an Industry/University relations program. In 1985, Dr. Savage led the development of What'sBest!, linking linear programming to spreadsheets. This popular package won PC Magazine's Technical Excellence Award. In 1990 he developed a successful series of seminars on analytical modeling in spreadsheets, of which "Practical Risk Modeling in Spreadsheets" is the latest. Dr. Savage is founder and president of AnalyCorp Inc., through which he consults and lectures extensively to business and government agencies.

1. ANALYTICAL MODELING IN SPREADSHEETS. Introduction. The Technology of Decision Making. Disciplined Intuition: A Philosophy. Analytical Models. Tutorial: Important Modeling Techniques. Understanding the Elements of a Worksheet Model. Separation of Data and Formulas. Making Sure the Model is Scalable. Experimenting with the Model. The Voices of Experience. The Pros and Cons of Spreadsheet Modeling. First the Cons. Now the Pros. 2. THE BUILDING BLOCKS OF UNCERTAINTY: RANDOM VARIABLES. Introduction. From Manhattan Project to Wall Street. XLSim(R). Tutorial:Estimating Profit with Monte Carlo Simulation. An Example: Uncertain Profit. Monte Carlo Simulation: The Basic Steps. The Building Blocks of Uncertainty. Uncertain Numbers: Random Variables. Averages of Uncertain Numbers: Diversification and the Central Limit Theorem. Important Classes of Uncertain Numbers: An Investment Example. Risk vs. Uncertainty: Risk Management. Value at Risk: Managing Risk in the Investment Example. Conclusion. 3. THE BUILDINGS OF UNCERTAINTY: FUNCTIONS OF RANDOM VARIABLES. Introduction. Tutorial: Estimating Inventory Costs Given Uncertain Demand. An Inventory Problem. Simulating the Cost. Simulating Results. The Flaw of Averages. The Buildings of Uncertainty. Worksheet Models Based on Uncertain Numbers: Functions of Random Variables. Experimenting Under Uncertainty: Parameterized Simulation. The Increase of Option Prices with Uncertainty: Implied Volatility. Uncertain Numbers That Are Related to Each Other: Statistical Dependence. The Connection with Linear Regression. Portfolios of Correlated Investments. How Many Trials Are Enough? Convergence. Sensitivity Analysis: The Big Picture. Hypothesis Testing: Did it Happen by Chance. Conclusion. 4. UNCERTAINTIES THAT EVOLVE OVER TIME. Introduction. Systems That Evolve Over Time. QUEUE.xla and Q_NET.xla. Simulation Through Time: Discrete-Event Simulation. A Fixed-Time-Incremented Simluation of a Forest Fire. Cellular Automata. Queuing Models. Classifying Queues. Fixed- versus Event-Incremented Time. Queuing Networks. The Extend? Discrete Event Simluation Software. Combining Excel Models with Extend. Markov Chains. An Example: Market Share. MARKOV.xls. A Remarkable Property of Markov Chains. Modifying the Transition Matrix to Evaluate Replacement Strategy. Conclusion. 5. FORECASTING. Introduction. Causal Forecasting. Time Series Analysis. Using Excel?s Regression and XLForecast. Tutorials: Regression and Time Series Analysis. Regression: Estimating Sales Based on Advertising Level. Time Series Analysis: Predicting Future Sales Based on Past History. The Importance of Errors. Errors Generated by Regression. Errors Generated by Time Series. Predicting the Past. Conclusions. Explanation of Regression and Exponential Smoothing. Regression. Exponential Smoothing. 6. DECISION TREES. Introduction. An Example: Ice Cream and Parking Tickets. Good Decisions versus Good Outcomes. XLTree. Tutorial: Building a Decision Tree. Experimental Drug Development. Building a Decision Tree with XLTree. Decision Analysis: Basic Concepts. Utility. Probability. Expected Value. Decision Forks. Uncertainty Forks. Sensitivity Analysis. Conditional Property. The Value of Information. State Variables. Mustering the Courage of Your Convictions. 7. OVERVIEW OF OPTIMIZATION. Introduction. The ABC?s of Optimization. Tutorial: Maximum Profit. How Many Boats to Produce? The ABC?s of Optimization. Interacting with the Model: What?sBest! The D?s of Optimization: Dual Values. Basic Optimization Examples. Product Mix. Blending. Staff Scheduling. Transportation. Network Flow Models. Conclusion. 8. EXTENSIONS OF OPTIMIZATION. Extending the Value of Optimization. Integer Variables. Combining Optimization Models: An Object Oriented Approach. Optimization Under Uncertainty. Nonlinear Optimization. Combinatorial Optimization. Complete Evaluation Times for N-City Traveling Salesman Problem. Common Errors in Optimization Models. Linear and Nonlinear Formulas. Improper Constraints. Local Maxima or Minima in Nonlinear Optimization. The Basics of Optimization Theory. Optimizing a Simplified BOAT Problem. Linear versus Nonlinear Problems. More on Dual Values. Conclusion. Appendix A: Queuing Equations: QUEUE.xla and Q_NET.xla. Appendix B: Software Command Reference. XLSim(R). QUEUE.xla and Q_NET.xla. Extend?. XLForecast?. XLTree?. Optimization Software. References. Index. Software Contained on the CD-ROM.

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