Spreadsheet Modeling and Decision Analysis 9th edition

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• Chapter 1: Introduction to Modeling and Decision Analysis
  • 1.0: Introduction
  • 1.1: The Modeling Approach to Decision Making
  • 1.2: Characteristics and Benefits of Modeling
  • 1.3: Mathematical Models
  • 1.4: Categories of Mathematical Models
  • 1.5: Business Analytics and the Problem-Solving Process
  • 1.6: Anchoring and Framing Effects
  • 1.7: Good Decisions vs. Good Outcomes
  • 1.8: Summary
  • 1.9: References
  • 1: Questions and Problems
  • 1: Test Bank
  
  
• Chapter 2: Introduction to Optimization and Linear Programming
  • 2.0: Introduction
  • 2.1: Applications of Mathematical Optimization
  • 2.2: Characteristics of Optimization Problems
  • 2.3: Expressing Optimization Problems Mathematically
  • 2.4: Mathematical Programming Techniques
  • 2.5: An Example LP Problem
  • 2.6: Formulating LP Models
  • 2.7: Summary of the LP Model for the Example Problem
  • 2.8: The General Form of an LP Model
  • 2.9: Solving LP Problems: An Intuitive Approach
  • 2.10: Solving LP Problems: A Graphical Approach
  • 2.11: Special Conditions in LP Models
  • 2.12: Summary
  • 2.13: References
  • 2: Questions and Problems
  • 2: Test Bank
  
  
• Chapter 3: Modeling and Solving LP Problems in a Spreadsheet
  • 3.0: Introduction
  • 3.1: Spreadsheet Solvers
  • 3.2: Solving LP Problems in a Spreadsheet
  • 3.3: The Steps in Implementing an LP Model in a Spreadsheet
  • 3.4: A Spreadsheet Model for the Blue Ridge Hot Tubs Problem
  • 3.5: How Solver Views the Model
  • 3.6: Using Analytic Solver
  • 3.7: Using Excel's Built-in Solver
  • 3.8: Goals and Guidelines for Spreadsheet Design
  • 3.9: Make vs. Buy Decisions
  • 3.10: An Investment Problem
  • 3.11: A Transportation Problem
  • 3.12: A Blending Problem
  • 3.13: A Production and Inventory Planning Problem
  • 3.14: A Multiperiod Cash Flow Problem
  • 3.15: Data Envelopment Analysis
  • 3.16: Summary
  • 3.17: References
  • 3: Questions and Problems
  • 3: Test Bank
  
  
• Chapter 4: Sensitivity Analysis and the Simplex Method
  • 4.0: Introduction
  • 4.1: The Purpose of Sensitivity Analysis
  • 4.2: Approaches to Sensitivity Analysis
  • 4.3: An Example Problem
  • 4.4: The Answer Report
  • 4.5: The Sensitivity Report
  • 4.6: Ad Hoc Sensitivity analysis
  • 4.7: Robust Optimization
  • 4.8: The Simplex Method
  • 4.9: Summary
  • 4.10: References
  • 4: Questions and Problems
  • 4: Test Bank
  
  
• Chapter 5: Network Modeling
  • 5.0: Introduction
  • 5.1: The Transshipment Problem
  • 5.2: The Shortest Path Problem
  • 5.3: The Equipment Replacement Problem
  • 5.4: Transportation/Assignment Problems
  • 5.5: Generalized Network Flow Problems
  • 5.6: Maximal Flow Problems
  • 5.7: Special Modeling Considerations
  • 5.8: Minimal Spanning Tree Problems
  • 5.9: Summary
  • 5.10: References
  • 5: Questions and Problems
  • 5: Test Bank
  
  
• Chapter 6: Integer Linear Programming
  • 6.0: Introduction
  • 6.1: Integrality Conditions
  • 6.2: Relaxation
  • 6.3: Solving the Relaxed Problem
  • 6.4: Bounds
  • 6.5 Rounding
  • 6.6: Stopping Rules
  • 6.7: Solving ILP Problems Using Solver
  • 6.8: Other ILP Problems
  • 6.9: An Employee Scheduling Problem
  • 6.10: Binary Variables
  • 6.11: A Capital Budgeting Problem
  • 6.12: Binary Variables and Logical Conditions
  • 6.13: The Line Balancing Problem
  • 6.14: The Fixed-Charge Problem
  • 6.15: Minimum Order/Purchase Size
  • 6.16: Quantity Discounts
  • 6.17: A Contract Award Problem
  • 6.18: The Branch-and-Bound Algorithm (Optional)
  • 6.19: Summary
  • 6.20: References
  • 6: Questions and Problems
  • 6: Test Bank
  
  
• Chapter 7: Goal Programming and Multiple Objective Optimization
  • 7.0: Introduction
  • 7.1: Goal Programming
  • 7.2: A Goal Programming Example
  • 7.3: Comments about Goal Programming
  • 7.4: Multiple Objective Optimization
  • 7.5: An MOLP Example
  • 7.6: Comments on MOLP
  • 7.7: Summary
  • 7.8: References
  • 7: Questions and Problems
  • 7: Test Bank
  
  
• Chapter 8: Nonlinear Programming and Evolutionary Optimization
  • 8.0: Introduction
  • 8.1: The Nature of NLP Problems
  • 8.2: Solution Strategies for NLP Problems
  • 8.3: Local vs. Global Optimal Solutions
  • 8.4: Economic Order Quantity Models
  • 8.5: Location Problems
  • 8.6: Nonlinear Network Flow Problem
  • 8.7: Project Selection Problems
  • 8.8: Optimizing Existing Financial Spreadsheet Models
  • 8.9: The Portfolio Selection Problems
  • 8.10: Sensitivity Analysis
  • 8.11: Solver Options for Solving NLP's
  • 8.12: Evolutionary Algorithms
  • 8.13: Forming Fair Teams
  • 8.14: The Traveling Salesperson Problem
  • 8.15: Summary
  • 8.16: References
  • 8: Questions and Problems
  • 8: Test Bank
  
  
• Chapter 9: Regression Analysis
  • 9.0: Introduction
  • 9.1: An Example
  • 9.2: Regression Models
  • 9.3: Simple Linear Regression Analysis
  • 9.4: Defining ""Best Fit""
  • 9.5: Solving the Problem Using Solver
  • 9.6: Solving the Problem Using the Regression Tool
  • 9.7: Evaluating the Fit
  • 9.8: The R² Statistic
  • 9.9: Making Predictions
  • 9.10: Statistical Tests for Population Parameters
  • 9.11: Introduction to Multiple Regression
  • 9.12: A Multiple Regression Example
  • 9.13: Selecting the Model
  • 9.14: Making Predictions
  • 9.15: Other Model Selection Issues
  • 9.16: Binary Independent Variables
  • 9.17: Statistical Tests for the Population Parameters
  • 9.18: Polynomial Regression
  • 9.19: Summary
  • 9.20: References
  • 9: Questions and Problems
  • 9: Test Bank
  
  
• Chapter 10: Data Mining
  • 10.0: Introduction
  • 10.1: Data Mining Overview
  • 10.2: Classification
  • 10.3: Classification Data Partitioning
  • 10.4: Discriminant Analysis
  • 10.5: Logistic Regression
  • 10.6: k-Nearest Neighbor
  • 10.7: Classification Trees
  • 10.8: Neural Networks
  • 10.9: Naïve Bayes
  • 10.10: Comments on Classification
  • 10.11: Prediction
  • 10.12: Association Rules (Affinity Analysis)
  • 10.13: Cluster Analysis
  • 10.14: Time Series
  • 10.15: Summary
  • 10.16: References
  • 10: Questions and Problems
  • 10: Test Bank
  
  
• Chapter 11: Time Series Forecasting
  • 11.0: Introduction
  • 11.1: Time Series Methods
  • 11.2: Measuring Accuracy
  • 11.3: Stationary Models
  • 11.4: Moving Averages
  • 11.5: Weighted Moving Averages
  • 11.6: Exponential Smoothing
  • 11.7: Seasonality
  • 11.8: Stationary Data with Additive Seasonal Effects
  • 11.9: Stationary Data with Multiplicative Seasonal Effects
  • 11.10: Trend Models
  • 11.11: Double Moving Average
  • 11.12: Double Exponential Smoothing (Holt's Method)
  • 11.13: Holt-Winter's Method for Additive Seasonal Effects
  • 11.14: Holt-Winter's Method for Multiplicative Seasonal Effects
  • 11.15: Modeling Time Series Trends Using Regression
  • 11.16: Linear Trend Model
  • 11.17: Quadratic Trend Model
  • 11.18: Modeling Seasonality with Regression Models
  • 11.19: Adjusting Trend Predictions with Seasonal Indices
  • 11.20: Seasonal Regression Models
  • 11.21: Combining Forecasts
  • 11.22: Summary
  • 11.23: References
  • 11: Questions and Problems
  • 11: Test Bank
  
  
• Chapter 12: Introduction to Simulation Using Analytic Solver
  • 12.0: Introduction
  • 12.1: Introduction
  • 12.2: Why Analyze Risk?
  • 12.3: Methods of Risk Analysis
  • 12.4: A Corporate health Insurance Example
  • 12.5: Spreadsheet Simulation Using Analytic Solver
  • 12.6: Random Number Generators
  • 12.7: Preparing the Model for Simulation
  • 12.8: Running the Simulation
  • 12.9: Data Analysis
  • 12.10: The Uncertainty of Sampling
  • 12.11: Interactive Simulation
  • 12.12: the Benefits of Simulation
  • 12.13: Additional Uses of Simulation
  • 12.14: A Reservation Management Example
  • 12.15: An Inventory Control Example
  • 12.16: A Project Selection Example
  • 12.17: A Portfolio Optimization Example
  • 12.18: Summary
  • 12.19: References
  • 12: Questions and Problems
  • 12: Test Bank
  
  
• Chapter 13: Queuing Theory
  • 13.0: Introduction
  • 13.1: The Purpose of Queuing Models
  • 13.2: Queuing System Configurations
  • 13.3: Characteristics of Queuing Systems
  • 13.4: Kendall Notation
  • 13.5: Queuing Models
  • 13.6: The M/M/s Model
  • 13.7: The M/M/s Model with Finite Queue Length
  • 13.8: The M/M/s Model with Finite Population
  • 13.9: The M/G/1 Model
  • 13.10: The M/D/1 Model
  • 13.11: Simulating Queues and the Steady-State Assumption
  • 13.12: Summary
  • 13.13: References
  • 13: Questions and Problems
  • 13: Test Bank
  
  
• Chapter 14: Decision Analysis
  • 14.0: Introduction
  • 14.1: Good Decisions vs. Good Outcomes
  • 14.2: Characteristics of Decision Problems
  • 14.3: An Example
  • 14.4: The Payoff Matrix
  • 14.5: Decision Rules
  • 14.6: Nonprobabilistic Methods
  • 14.7: Probabilistic Methods
  • 14.8: The Expected Value of Perfect Information
  • 14.9: Decision Trees
  • 14.10: Creating Decision Trees with Analytic Solver
  • 14.11: Multistage Decision Problems
  • 14.12: Sensitivity Analysis
  • 14.13: Using Sample Information in Decision Making
  • 14.14: Computing Conditional Probabilities
  • 14.15: Utility Theory
  • 14.16: Multicriteria Decision Making
  • 14.17: The Multicriteria Scoring Model
  • 14.18: The Analytic Hierarchy Process
  • 14.19: Summary
  • 14.20: References
  • 14: Questions and Problems
  • 14: Test Bank
  
  
• Chapter 15: Project Management
  • 15.0: Introduction
  • 15.1: An Example
  • 15.2: Creating the Project Network
  • 15.3: CPM: An Overview
  • 15.4: The Forward Pass
  • 15.5: The Backward Pass
  • 15.6: Determining the Critical Path
  • 15.7: Project Management Using Spreadsheets
  • 15.8: Gantt Charts
  • 15.9: Project Crashing
  • 15.10: Pert: An Overview
  • 15.11: Simulating Project Networks
  • 15.12: Microsoft Project
  • 15.13: Summary
  • 15.14: References
  • 15: Questions and Problems
  • 15: Test Bank
  
  
• Chapter A: Appendix
  • A: Appendix A: Basics of Excel