Data Analysis, Optimization, and Simulation Modeling

Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 1975. Wayne received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, PRATICAL MANAGEMENT SCIENCE, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, AND FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance and marketing. S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. His current interest is in spreadsheet modeling, including development of VBA applications in Excel(R). Dr. Albright has published more than 20 articles in leading operations research journals in the area of applied probability. He has also published a number of successful textbooks, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, and SPREADSHEET MODELING AND APPLICATIONS. Christopher J. Zappe earned his B.A. in Mathematics from DePauw University in 1983 and his M.B.A. and Ph.D. in Decision Sciences from Indiana University in 1987 and 1988, respectively. Since 1993, Professor Zappe has been serving as an associate professor in the decision sciences area of the Department of Management at Bucknell University (Lewisburg, PA). He has published articles in scholarly journals such as Managerial and Decision Economics, OMEGA, Naval Research Logistics, and Interfaces.

Preface 1. Introduction to Data Analysis and Decision Making. 1.1. Introduction. 1.2. An Overview of the Book. 1.3. Modeling and Models. 1.4. Conclusion. PART I: EXPLORING DATA. 2. Describing the Distribution of a Single Variable. 2.1 Introduction. 2.2 Basic Concepts. 2.3 Descriptive Measures for Categorical Variables. 2.4 Descriptive Measures for Numerical Variables. 2.5 Time Series Data. 2.6 Outliers and Missing Values. 2.7 Excel Tables for Filtering, Sorting, and Summarizing. 2.8 Conclusion. 3. Finding Relationships Among Variables. 3.1 Introduction. 3.2 Relationships Among Categorical Variables. 3.3 Relationships Among Categorical Variables and a Numerical Variable. 3.4 Relationships Among Numerical Variables. 3.5 Pivot Tables. 3.6 An Extended Example. 3.7 Conclusion. PART II: PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY 4. Probability and Probability Distributions. 4.1. Introduction. 4.2. Probability Essentials. 4.3. Distribution of a Single Random Variable. 4.4. An Introduction to Simulation. 4.5. Distribution of Two Random Variables: Scenario Approach. 4.6. Distribution of Two Random Variables: Joint Probability Approach. 4.7. Independent Random Variables. 4.8. Weighted Sums of Random Variables. 4.9. Conclusion. 5. Normal, Binomial, Poisson, and Exponential Distributions. 5.1. Introduction. 5.2. The Normal Distribution. 5.3. Applications of the Normal Distribution. 5.4. The Binomial Distribution. 5.5. Applications of the Binomial Distribution. 5.6. The Poisson and Exponential Distributions. 5.7. Fitting a Probability Distribution to Data with @RISK. 5.8. Conclusion. 6. Decision Making Under Uncertainty. 6.1. Introduction. 6.2. Elements of a Decision Analysis. 6.3. The PrecisionTree Add-In. 6.4. Bayes' Rule. 6.5. Multistage Decision Problems. 6.6. Incorporating Attitudes Toward Risk. 6.7. Conclusion. PART III: STATISTICAL INFERENCE. 7. Sampling and Sampling Distributions. 7.1. Introduction. 7.2. Sampling Terminology. 7.3. Methods for Selecting Random Samples. 7.4. An Introduction to Estimation. 7.5. Conclusion. 8. Confidence Interval Estimation. 8.1. Introduction. 8.2. Sampling Distributions. 8.3. Confidence Interval for a Mean. 8.4. Confidence Interval for a Total. 8.5. Confidence Interval for a Proportion. 8.6. Confidence Interval for a Standard Deviation. 8.7. Confidence Interval for the Difference Between Means. 8.8. Confidence Interval for the Difference Between Proportions. 8.9. Controlling Confidence Interval Length. 8.10. Conclusion. 9. Hypothesis Testing. 9.1. Introduction. 9.2. Concepts in Hypothesis Testing. 9.3. Hypothesis Tests for a Population Mean. 9.4. Hypothesis Tests for Other Parameters. 9.5. Tests for Normality. 9.6. Chi-Square Test for Independence. 9.7. One-Way ANOVA. 9.8. Conclusion. PART IV: REGRESSION ANALYSIS AND TIME SERIES FORECASTING. 10. Regression Analysis: Estimating Relationships. 10.1. Introduction. 10.2. Scatterplots: Graphing Relationships. 10.3. Correlations: Indicators of Linear Relationships. 10.4. Simple Linear Regression. 10.5. Multiple Regression. 10.6. Modeling Possibilities. 10.7. Validation of the Fit. 10.8. Conclusion. 11. Regression Analysis: Statistical Inference. 11.1. Introduction. 11.2. The Statistical Model. 11.3. Inferences About the Regression Coefficients. 11.4. Multicollinearity. 11.5. Include/Exclude Decisions. 11.6. Stepwise Regression. 11.7. The Partial F Test. 11.8. Outliers. 11.9. Violations of Regression Assumptions. 11.10. Prediction. 11.11. Conclusion. 12. Time Series Analysis and Forecasting. 12.1. Introduction. 12.2. Forecasting Methods: An Overview. 12.3. Testing for Randomness. 12.4. Regression-Based Trend Models. 12.5. The Random Walk Model. 12.6. Autoregression Models. 12.7. Moving Averages. 12.8. Exponential Smoothing. 12.9. Seasonal Models. 12.10. Conclusion. PART V: OPTIMIZATION AND SIMULATION MODELING. 13. Introduction to Optimization Modeling. 13.1. Introduction. 13.2. Introduction to Optimization. 13.3. A Two-Variable Product Mix Model. 13.4. Sensitivity Analysis. 13.5. Properties of Linear Models. 13.6. Infeasibility and Unboundedness. 13.7. A Larger Product Mix Model. 13.8. A Multiperiod Production Model. 13.9. A Comparison of Algebraic and Spreadsheet Models. 13.10. A Decision Support System. 13.11. Conclusion. 14. Optimization Models. 14.1. Introduction. 14.2. Worker Scheduling Models. 14.3. Blending Models. 14.4. Logistics Models. 14.5. Aggregate Planning Models. 14.6. Financial Models. 14.7. Integer Programming Models. 14.8. Nonlinear Programming Models. 14.9. Conclusion. 15. Introduction to Simulation Modeling. 15.1. Introduction. 15.2. Probability Distributions for Input Variables. 15.3. Simulation and the Flaw of Averages. 15.4. Simulation with Built-In Excel Tools. 15.5. Introduction to the @RISK Add-in. 15.6. The Effects of Input Distributions on Results. 15.7. Conclusion. 16. Simulation Models. 16.1. Introduction. 16.2. Operations Models. 16.3. Financial Models. 16.4. Marketing Models. 16.5. Simulating Games of Chance. 16.6. An Automated Template for @RISK Models. 16.7. Conclusion. PART VI: BONUS ONLINE MATERIAL 2 Using the Advanced Filter and Database Functions. 17. Importing Data into Excel. 17.1 Introduction. 17.2 Rearranging Excel Data. 17.3 Importing Text Data. 17.4 Importing Relational Database Data. 17.5 Web Queries. 17.6 Cleansing the Data. 17.7 Conclusion.