Data Analysis and Decision Making

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2 4TH EDITION Data Analysis and Decision Making S. Christian Albright Kelley School of Business, Indiana University Wayne L. Winston Kelley School of Business, Indiana University Christopher J. Zappe Bucknell University With cases by Mark Broadie Graduate School of Business, Columbia University Peter Kolesar Graduate School of Business, Columbia University Lawrence L. Lapin San Jose State University William D. Whisler California State University, Hayward Australia Brazil Japan Korea Mexico Singapore Spain United Kingdom United States

3 This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it. For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit to search by ISBN#, author, title, or keyword for materials in your areas of interest.

4 Data Analysis and Decision Making, Fourth Edition S. Christian Albright, Wayne L. Winston, Christopher J. Zappe Vice President of Editorial, Business: Jack W. Calhoun Publisher: Joe Sabatino Sr. Acquisitions Editor: Charles McCormick, Jr. Sr. Developmental Editor: Laura Ansara Editorial Assistant: Nora Heink Marketing Manager: Adam Marsh Marketing Coordinator: Suellen Ruttkay Sr. Content Project Manager: Tim Bailey Media Editor: Chris Valentine Frontlist Buyer, Manufacturing: Miranda Klapper Sr. Marketing Communications Manager: Libby Shipp Production Service: MPS Limited, A Macmillan company Sr. Art Director: Stacy Jenkins Shirley Cover Designer: Lou Ann Thesing Cover Image: istock Photo 2011, 2009 South-Western, Cengage Learning ALL RIGHTS RESERVED. No part of this work covered by the copyright herein may be reproduced, transmitted, stored, or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, web distribution, information networks, or information storage and retrieval systems, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the publisher. For product information and technology assistance, contact us at Cengage Learning Customer & Sales Support For permission to use material from this text or product, submit all requests online at Further permissions questions can be ed to ExamView is a registered trademark of einstruction Corp. Microsoft and Excel spreadsheet software are registered trademarks of Microsoft Corporation used herein under license. Library of Congress Control Number: Student Edition Package ISBN 13: Student Edition Package ISBN 10: Student Edition ISBN 13: Student Edition ISBN 10: South-Western Cengage Learning 5191 Natorp Boulevard Mason, OH USA Cengage Learning products are represented in Canada by Nelson Education, Ltd. For your course and learning solutions, visit Purchase any of our products at your local college store or at our preferred online store Printed in the United States of America

5 To my wonderful family To my wonderful wife Mary my best friend and constant companion; to Sam, Lindsay, and Teddy, our new and adorable grandson; and to Bryn, our wild and crazy Welsh corgi, who can t wait for Teddy to be able to play ball with her! S.C.A To my wonderful family W.L.W. To my wonderful family Jeannie, Matthew, and Jack. And to my late sister, Jenny, and son, Jake, who live eternally in our loving memories. C.J.Z.

6 About the Authors S. Christian Albright got his B.S. degree in Mathematics from Stanford in 1968 and his Ph.D. in Operations Research from Stanford in Since then he has been teaching in the Operations & Decision Technologies Department in the Kelley School of Business at Indiana University (IU). He has taught courses in management science, computer simulation, statistics, and computer programming to all levels of business students: undergraduates, MBAs, and doctoral students. In addition, he has taught simulation modeling at General Motors and Whirlpool, and he has taught database analysis for the Army. He has published over 20 articles in leading operations research journals in the area of applied probability, and he has authored the books Statistics for Business and Economics, Practical Management Science, Spreadsheet Modeling and Applications, Data Analysis for Managers, and VBA for Modelers. He also works with the Palisade Corporation on the commercial version, StatTools, of his statistical StatPro add-in for Excel. His current interests are in spreadsheet modeling, the development of VBA applications in Excel, and programming in the.net environment. On the personal side, Chris has been married for 39 years to his wonderful wife, Mary, who retired several years ago after teaching 7th grade English for 30 years and is now working as a supervisor for student teachers at IU. They have one son, Sam, who lives in Philadelphia with his wife Lindsay and their newly born son Teddy. Chris has many interests outside the academic area. They include activities with his family (especially traveling with Mary), going to cultural events at IU, power walking while listening to books on his ipod, and reading. And although he earns his livelihood from statistics and management science, his real passion is for playing classical piano music. Wayne L. Winston is Professor of Operations & Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 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 Practical Management Science, Data Analysis and Decision Making, and Financial Models Using Simulation and Optimization. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the schoolwide MBA award four times. He has taught classes at Microsoft, GM, Ford, Eli Lilly, Bristol-Myers Squibb, Arthur Andersen, Roche, PricewaterhouseCoopers, and NCR. His current interest is showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance and marketing. Wayne enjoys swimming and basketball, and his passion for trivia won him an appearance several years ago on the television game show Jeopardy, where he won two games. He is married to the lovely and talented Vivian. They have two children, Gregory and Jennifer. 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. Between 1988 and 1993, he performed research and taught various decision sciences courses at the University of Florida in the College of Business Administration. From 1993 until 2010, Professor Zappe taught decision sciences in the Department of Management at Bucknell University, and in 2010, he was named provost at Gettysburg College. Professor Zappe has taught undergraduate courses in business statistics, decision modeling and analysis, and computer simulation. He also developed and taught a number of interdisciplinary Capstone Experience courses and Foundation Seminars in support of the Common Learning Agenda at Bucknell. Moreover, he has taught advanced seminars in applied game theory, system dynamics, risk assessment, and mathematical economics. He has published articles in scholarly journals such as Managerial and Decision Economics, OMEGA, Naval Research Logistics, and Interfaces. iv

7 Brief Contents Preface xii 1 Introduction to Data Analysis and Decision Making 1 Part 1 Exploring Data 19 2 Describing the Distribution of a Single Variable 21 3 Finding Relationships among Variables 85 Part 2 Probability and Decision Making under Uncertainty Probability and Probability Distributions Normal, Binomial, Poisson, and Exponential Distributions Decision Making under Uncertainty 273 Part 3 Statistical Inference Sampling and Sampling Distributions Confidence Interval Estimation Hypothesis Testing 455 Part 4 Regression Analysis and Time Series Forecasting Regression Analysis: Estimating Relationships Regression Analysis: Statistical Inference Time Series Analysis and Forecasting 669 Part 5 Optimization and Simulation Modeling Introduction to Optimization Modeling Optimization Models Introduction to Simulation Modeling Simulation Models 987 Part 6 Online Bonus Material 2 Using the Advanced Filter and Database Functions Importing Data into Excel 17-1 Appendix A Statistical Reporting A-1 References 1055 Index 1059 v

8 Contents Preface xii 1 Introduction to Data Analysis and Decision Making Introduction An Overview of the Book The Methods The Software Modeling and Models Graphical Models Algebraic Models Spreadsheet Models A Seven-Step Modeling Process Conclusion 16 CASE 1.1 Entertainment on a Cruise Ship 17 PART 1 EXPLORING DATA 19 2 Describing the Distribution of a Single Variable Introduction Basic Concepts Populations and Samples Data Sets, Variables, and Observations Types of Data Descriptive Measures for Categorical Variables Descriptive Measures for Numerical Variables Numerical Summary Measures Numerical Summary Measures with StatTools Charts for Numerical Variables Time Series Data Outliers and Missing Values Outliers Missing Values Excel Tables for Filtering, Sorting, and Summarizing Filtering Conclusion 75 CASE 2.1 Correct Interpretation of Means 81 CASE 2.2 The Dow Jones Industrial Average 82 CASE 2.3 Home and Condo Prices 83 3 Finding Relationships among Variables Introduction Relationships among Categorical Variables Relationships among Categorical Variables and a Numerical Variable Stacked and Unstacked Formats Relationships among Numerical Variables Scatterplots Correlation and Covariance Pivot Tables An Extended Example Conclusion 144 CASE 3.1 Customer Arrivals at Bank CASE 3.2 Saving, Spending, and Social Climbing 150 CASE 3.3 Churn in the Cellular Phone Market 151 PART 2 PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY Probability and Probability Distributions Introduction Probability Essentials Rule of Complements Addition Rule Conditional Probability and the Multiplication Rule Probabilistic Independence 162 vi

9 4.2.5 Equally Likely Events Subjective Versus Objective Probabilities Distribution of a Single Random Variable Conditional Mean and Variance An Introduction to Simulation Distribution of Two Random Variables: Scenario Approach Distribution of Two Random Variables: Joint Probability Approach How to Assess Joint Probability Distributions Independent Random Variables Weighted Sums of Random Variables Conclusion 200 CASE 4.1 Simpson s Paradox Normal, Binomial, Poisson, and Exponential Distributions Introduction The Normal Distribution Continuous Distributions and Density Functions The Normal Density Standardizing: Z-Values Normal Tables and Z-Values Normal Calculations in Excel Empirical Rules Revisited Applications of the Normal Distribution The Binomial Distribution Mean and Standard Deviation of the Binomial Distribution The Binomial Distribution in the Context of Sampling The Normal Approximation to the Binomial Applications of the Binomial Distribution The Poisson and Exponential Distributions The Poisson Distribution The Exponential Distribution Fitting a Probability Distribution to Data Conclusion 261 CASE 5.1 EuroWatch Company 269 CASE 5.2 Cashing in on the Lottery Decision Making under Uncertainty Introduction Elements of Decision Analysis Payoff Tables Possible Decision Criteria Expected Monetary Value (EMV) Sensitivity Analysis Decision Trees Risk Profiles The PrecisionTree Add-In Bayes Rule Multistage Decision Problems The Value of Information Incorporating Attitudes Toward Risk Utility Functions Exponential Utility Certainty Equivalents Is Expected Utility Maximization Used? Conclusion 331 CASE 6.1 Jogger Shoe Company 345 CASE 6.2 Westhouser Parer Company 346 CASE 6.3 Biotechnical Engineering 347 PART 3 STATISTICAL INFERENCE Sampling and Sampling Distributions Introduction Sampling Terminology Methods for Selecting Random Samples Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling Multistage Sampling Schemes 365 Contents vii

10 7.4 An Introduction to Estimation Sources of Estimation Error Key Terms in Sampling Sampling Distribution of the Sample Mean The Central Limit Theorem Sample Size Determination Summary of Key Ideas for Simple Random Sampling Conclusion 382 CASE 7.1 Sampling from DVD Movie Renters Confidence Interval Estimation Introduction Sampling Distributions The t Distribution Other Sampling Distributions Confidence Interval for a Mean Confidence Interval for a Total Confidence Interval for a Proportion Confidence Interval for a Standard Deviation Confidence Interval for the Difference between Means Independent Samples Paired Samples Confidence Interval for the Difference between Proportions Controlling Confidence Interval Length Sample Size for Estimation of the Mean Sample Size for Estimation of Other Parameters Conclusion 441 CASE 8.1 Harrigan University Admissions 449 CASE 8.2 Employee Retention at D&Y 450 CASE 8.3 Delivery Times at SnowPea Restaurant 451 CASE 8.4 The Bodfish Lot Cruise Hypothesis Testing Introduction Concepts in Hypothesis Testing Null and Alternative Hypotheses One-Tailed Versus Two-Tailed Tests Types of Errors Significance Level and Rejection Region Significance from p-values Type II Errors and Power Hypothesis Tests and Confidence Intervals Practical Versus Statistical Significance Hypothesis Tests for a Population Mean Hypothesis Tests for Other Parameters Hypothesis Tests for a Population Proportion Hypothesis Tests for Differences between Population Means Hypothesis Test for Equal Population Variances Hypothesis Tests for Differences between Population Proportions Tests for Normality Chi-Square Test for Independence One-Way ANOVA Conclusion 513 CASE 9.1 Regression Toward the Mean 519 CASE 9.2 Baseball Statistics 520 CASE 9.3 The Wichita Anti Drunk Driving Advertising Campaign 521 CASE 9.4 Deciding Whether to Switch to a New Toothpaste Dispenser 523 CASE 9.5 Removing Vioxx from the Market 526 PART 4 REGRESSION ANALYSIS AND TIME SERIES FORECASTING Regression Analysis: Estimating Relationships Introduction 531 viii Contents

11 10.2 Scatterplots: Graphing Relationships Linear Versus Nonlinear Relationships Outliers Unequal Variance No Relationship Correlations: Indicators of Linear Relationships Simple Linear Regression Least Squares Estimation Standard Error of Estimate The Percentage of Variation Explained: R Multiple Regression Interpretation of Regression Coefficients Interpretation of Standard Error of Estimate and R Modeling Possibilities Dummy Variables Interaction Variables Nonlinear Transformations Validation of the Fit Conclusion 588 CASE 10.1 Quantity Discounts at the Firm Chair Company 596 CASE 10.2 Housing Price Structure in Mid City 597 CASE 10.3 Demand for French Bread at Howie s Bakery 598 CASE 10.4 Investing for Retirement Regression Analysis: Statistical Inference Introduction The Statistical Model Inferences about the Regression Coefficients Sampling Distribution of the Regression Coefficients Hypothesis Tests for the Regression Coefficients and p-values A Test for the Overall Fit: The ANOVA Table Multicollinearity Include/Exclude Decisions Stepwise Regression The Partial F Test Outliers Violations of Regression Assumptions Nonconstant Error Variance Nonnormality of Residuals Autocorrelated Residuals Prediction Conclusion 653 CASE 11.1 The Artsy Corporation 663 CASE 11.2 Heating Oil at Dupree Fuels Company 665 CASE 11.3 Developing a Flexible Budget at the Gunderson Plant 666 CASE 11.4 Forecasting Overhead at Wagner Printers Time Series Analysis and Forecasting Introduction Forecasting Methods: An Overview Extrapolation Methods Econometric Models Combining Forecasts Components of Time Series Data Measures of Accuracy Testing for Randomness The Runs Test Autocorrelation Regression-Based Trend Models Linear Trend Exponential Trend The Random Walk Model Autoregression Models Moving Averages Exponential Smoothing Simple Exponential Smoothing Holt s Model for Trend Seasonal Models 720 Contents ix

12 Winters Exponential Smoothing Model Deseasonalizing: The Ratio-to-Moving- Averages Method Estimating Seasonality with Regression Conclusion 735 CASE 12.1 Arrivals at the Credit Union 740 CASE 12.2 Forecasting Weekly Sales at Amanta 741 PART 5 OPTIMIZATION AND SIMULATION MODELING Introduction to Optimization Modeling Introduction Introduction to Optimization A Two-Variable Product Mix Model Sensitivity Analysis Solver s Sensitivity Report SolverTable Add-In Comparison of Solver s Sensitivity Report and SolverTable Properties of Linear Models Proportionality Additivity Divisibility Discussion of Linear Properties Linear Models and Scaling Infeasibility and Unboundedness Infeasibility Unboundedness Comparison of Infeasibility and Unboundedness A Larger Product Mix Model A Multiperiod Production Model A Comparison of Algebraic and Spreadsheet Models A Decision Support System Conclusion 799 CASE 13.1 Shelby Shelving 807 CASE 13.2 Sonoma Valley Wines Optimization Models Introduction Worker Scheduling Models Blending Models Logistics Models Transportation Models Other Logistics Models Aggregate Planning Models Financial Models Integer Programming Models Capital Budgeting Models Fixed-Cost Models Set-Covering Models Nonlinear Programming Models Basic Ideas of Nonlinear Optimization Managerial Economics Models Portfolio Optimization Models Conclusion 905 CASE 14.1 Giant Motor Company 912 CASE 14.2 GMS Stock Hedging Introduction to Simulation Modeling Introduction Probability Distributions for Input Variables Types of Probability Distributions Common Probability Distributions to Explore Probability Distributions Simulation and the Flaw of Averages Simulation with Built-In Excel Tools Introduction to Add-in 953 Features Models with a Single Random Input Variable Some Limitations 963 Models with Several Random Input Variables 964 x Contents

13 15.6 The Effects of Input Distributions on Results Effect of the Shape of the Input Distribution(s) Effect of Correlated Input Variables Conclusion 978 CASE 15.1 Ski Jacket Production 985 CASE 15.2 Ebony Bath Soap Simulation Models Introduction Operations Models Bidding for Contracts Warranty Costs Drug Production with Uncertain Yield Financial Models Financial Planning Models Cash Balance Models Investment Models Marketing Models Models of Customer Loyalty Marketing and Sales Models Simulating Games of Chance Simulating the Game of Craps Simulating the NCAA Basketball Tournament An Automated Template Models Conclusion 1045 CASE 16.1 College Fund Investment 1053 CASE 16.2 Bond Investment Strategy 1054 PART 6 ONLINE BONUS MATERIAL 2 Using the Advanced Filter and Database Functions Importing Data into Excel Introduction Rearranging Excel Data Importing Text Data Importing Relational Database Data A Brief Introduction to Relational Databases Using Microsoft Query SQL Statements Web Queries Cleansing Data Conclusion CASE 17.1 EduToys, Inc Appendix A: Statistical Reporting A-1 A.1 Introduction A-1 A.2 Suggestions for Good Statistical Reporting A-2 A.2.1 Planning A-2 A.2.2 Developing a Report A-3 A.2.3 Be Clear A-4 A.2.4 Be Concise A-5 A.2.5 Be Precise A-5 A.3 Examples of Statistical Reports A-6 A.4 Conclusion A-18 References 1055 Index 1059 Contents xi

14 Preface With today s technology, companies are able to collect tremendous amounts of data with relative ease. Indeed, many companies now have more data than they can handle. However, the data are usually meaningless until they are analyzed for trends, patterns, relationships, and other useful information. This book illustrates in a practical way a variety of methods, from simple to complex, to help you analyze data sets and uncover important information. In many business contexts, data analysis is only the first step in the solution of a problem. Acting on the solution and the information it provides to make good decisions is a critical next step. Therefore, there is a heavy emphasis throughout this book on analytical methods that are useful in decision making. Again, the methods vary considerably, but the objective is always the same to equip you with decision-making tools that you can apply in your business careers. We recognize that the majority of students in this type of course are not majoring in a quantitative area. They are typically business majors in finance, marketing, operations management, or some other business discipline who will need to analyze data and make quantitative-based decisions in their jobs. We offer a hands-on, example-based approach and introduce fundamental concepts as they are needed. Our vehicle is spreadsheet software specifically, Microsoft Excel. This is a package that most students already know and will undoubtedly use in their careers. Our MBA students at Indiana University are so turned on by the required course that is based on this book that almost all of them (mostly finance and marketing majors) take at least one of our follow-up elective courses in spreadsheet modeling. We are convinced that students see value in quantitative analysis when the course is taught in a practical and example-based approach. Rationale for writing this book Data Analysis and Decision Making is different from the many fine textbooks written for statistics and management science. Our rationale for writing this book is based on three fundamental objectives. 1. Integrated coverage and applications. The book provides a unified approach to business-related problems by integrating methods and applications that have been traditionally taught in separate courses, specifically statistics and management science. 2. Practical in approach. The book emphasizes realistic business examples and the processes managers actually use to analyze business problems. The emphasis is not on abstract theory or computational methods. 3. Spreadsheet-based. The book provides students with the skills to analyze business problems with tools they have access to and will use in their careers. To this end, we have adopted Excel and commercial spreadsheet add-ins. Integrated coverage and applications In the past, many business schools, including ours at Indiana University, have offered a required statistics course, a required decision-making course, and a required management science course or some subset of these. One current trend, however, is to have only one required course that covers the basics of statistics, some regression analysis, some decision making under uncertainty, some linear programming, some simulation, and possibly others. Essentially, we faculty in the quantitative area get one opportunity to teach all business students, so we attempt to cover a variety of useful quantitative methods. We are not necessarily arguing that this trend is ideal, but rather that it is a reflection of the reality at our university and, we suspect, at many others. After several years of teaching this course, we have found it to be a great opportunity to attract students to the subject and more advanced study. The book is also integrative in another important aspect. It not only integrates a number of analytical methods, but it also applies them to a wide variety of business problems that is, it analyzes realistic examples from many business disciplines. We include examples, problems, and cases that deal with portfolio xii

15 optimization, workforce scheduling, market share analysis, capital budgeting, new product analysis, and many others. Practical in approach We want this book to be very example-based and practical. We strongly believe that students learn best by working through examples, and they appreciate the material most when the examples are realistic and interesting. Therefore, our approach in the book differs in two important ways from many competitors. First, there is just enough conceptual development to give students an understanding and appreciation for the issues raised in the examples. We often introduce important concepts, such as multicollinearity in regression, in the context of examples, rather than discussing them in the abstract. Our experience is that students gain greater intuition and understanding of the concepts and applications through this approach. Second, we place virtually no emphasis on hand calculations. We believe it is more important for students to understand why they are conducting an analysis and what it means than to emphasize the tedious calculations associated with many analytical techniques. Therefore, we illustrate how powerful software can be used to create graphical and numerical outputs in a matter of seconds, freeing the rest of the time for in-depth interpretation of the output, sensitivity analysis, and alternative modeling approaches. In our own courses, we move directly into a discussion of examples, where we focus almost exclusively on interpretation and modeling issues and let the software perform the number crunching. Spreadsheet-based teaching We are strongly committed to teaching spreadsheetbased, example-driven courses, regardless of whether the basic area is data analysis or management science. We have found tremendous enthusiasm for this approach, both from students and from faculty around the world who have used our books. Students learn and remember more, and they appreciate the material more. In addition, instructors typically enjoy teaching more, and they usually receive immediate reinforcement through better teaching evaluations. We were among the first to move to spreadsheet-based teaching almost two decades ago, and we have never regretted the move. What we hope to accomplish in this book Condensing the ideas in the above paragraphs, we hope to: Reverse negative student attitudes about statistics and quantitative methods by making these topics real, accessible, and interesting; Give students lots of hands-on experience with real problems and challenge them to develop their intuition, logic, and problem-solving skills; Expose students to real problems in many business disciplines and show them how these problems can be analyzed with quantitative methods; Develop spreadsheet skills, including experience with powerful spreadsheet add-ins, that add immediate value in students other courses and their future careers. New in the fourth edition There are two major changes in this edition. We have completely rewritten and reorganized Chapters 2 and 3. Chapter 2 now focuses on the description of one variable at a time, and Chapter 3 focuses on relationships between variables. We believe this reorganization is more logical. In addition, both of these chapters have more coverage of categorical variables, and they have new examples with more interesting data sets. We have made major changes in the problems, particularly in Chapters 2 and 3. Many of the problems in previous editions were either uninteresting or outdated, so in most cases we deleted or updated such problems, and we added a number of brand-new problems. We also created a file, essentially a database of problems, that is available to instructors. This file, Problem Database.xlsx, indicates the context of each of the problems, and it also shows the correspondence between problems in this edition and problems in the previous edition. Besides these two major changes, there are a number of smaller changes, including the following: Due to the length of the book, we decided to delete the old Chapter 4 (Getting the Right Preface xiii

16 Data) from the printed book and make it available online as Chapter 17. This chapter, now called Importing Data into Excel, has been completely rewritten, and its section on Excel tables is now in Chapter 2. (The old Chapters 5 17 were renumbered 4 16.) The book is still based on Excel 2007, but where it applies, notes about changes in Excel 2010 have been added. Specifically, there is a small section on the new slicers for pivot tables, and there are several mentions of the new statistical functions (although the old functions still work). Each chapter now has Conceptual Questions in the end-of-chapter section. There were a few Conceptual Exercises in some chapters in previous editions, but the new versions are more numerous, consistent, and relevant. The first two linear programming (LP) examples in Chapter 13 (the old Chapter 14) have been replaced by two product mix models, where the second is an extension of the first. Our thinking was that the previous diet-themed model was overly complex as a first LP example. Several of the chapter-opening vignettes have been replaced by newer and more interesting ones. There are now many short fundamental insights throughout the chapters. We hope these allow the students to step back from the details and see the really important ideas. Software This book is based entirely on Microsoft Excel, the spreadsheet package that has become the standard analytical tool in business. Excel is an extremely powerful package, and one of our goals is to convert casual users into power users who can take full advantage of its features. If we accomplish no more than this, we will be providing a valuable skill for the business world. However, Excel has some limitations. Therefore, this book includes several Excel add-ins that greatly enhance Excel s capabilities. As a group, these add-ins comprise what is arguably the most impressive assortment of spreadsheet-based software accompanying any book on the market. DecisionTools add-in. The textbook Web site for Data Analysis and Decision Making provides a link to the powerful DecisionTools Suite by Palisade Corporation. This suite includes seven separate add-ins, the first three of which we use an add-in for simulation StatTools, an add-in for statistical data analysis PrecisionTree, a graphical-based add-in for creating and analyzing decision trees TopRank, an add-in for performing what-if analyses RISKOptimizer, an add-in for performing optimization on simulation models NeuralTools, an add-in for finding complex, nonlinear relationships Evolver TM, an add-in for performing optimization on complex nonsmooth models Online access to the DecisionTools Suite, available with new copies of the book, is an academic version, slightly scaled down from the professional version that sells for hundreds of dollars and is used by many leading companies. It functions for two years when properly installed, and it puts only modest limitations on the size of data sets or models that can be analyzed. (Visit for specific details on these limitations.) We and PrecisionTree extensively in the chapters on simulation and decision making under uncertainty, and we use StatTools throughout all of the data analysis chapters. SolverTable add-in. We also include SolverTable, a supplement to Excel s built-in Solver for optimization. If you have ever had difficulty understanding Solver s sensitivity reports, you will appreciate SolverTable. It works like Excel s data tables, except that for each input (or pair of inputs), the add-in runs Solver and reports the optimal output values. SolverTable is used extensively in the optimization chapters. The version of SolverTable included in this book has been revised for Excel (Although SolverTable is available on this textbook s Web site, it is also available for free from the first author s Web site, Possible sequences of topics Although we use the book for our own required onesemester course, there is admittedly more material xiv Preface

17 than can be covered adequately in one semester. We have tried to make the book as modular as possible, allowing an instructor to cover, say, simulation before optimization or vice versa, or to omit either of these topics. The one exception is statistics. Due to the natural progression of statistical topics, the basic topics in the early chapters should be covered before the more advanced topics (regression and time series analysis) in the later chapters. With this in mind, there are several possible ways to cover the topics. For a one-semester required course, with no statistics prerequisite (or where MBA students have forgotten whatever statistics they learned years ago): If data analysis is the primary focus of the course, then Chapters 2 5, 7 11, and possibly the online Chapter 17 (all statistics and probability topics) should be covered. Depending on the time remaining, any of the topics in Chapters 6 (decision making under uncertainty), 12 (time series analysis), (optimization), or (simulation) can be covered in practically any order. For a one-semester required course, with a statistics prerequisite: Assuming that students know the basic elements of statistics (up through hypothesis testing, say), the material in Chapters 2 5 and 7 9 can be reviewed quickly, primarily to illustrate how Excel and add-ins can be used to do the number crunching. Then the instructor can choose among any of the topics in Chapters 6, 10 11, 12, 13 14, or (in practically any order) to fill the remainder of the course. For a two-semester required sequence: Given the luxury of spreading the topics over two semesters, the entire book can be covered. The statistics topics in Chapters 2 5 and 7 9 should be covered in order before other statistical topics (regression and time series analysis), but the remaining chapters can be covered in practically any order. Custom publishing If you want to use only a subset of the text, or add chapters from the authors other texts or your own materials, you can do so through Cengage Learning Custom Publishing. Contact your local Cengage Learning representative for more details. Student ancillaries Textbook Web Site Every new student edition of this book comes with an Instant Access Code (bound inside the book). The code provides access to the Data Analysis and Decision Making, 4e textbook Web site that links to all of the following files and tools: DecisionTools Suite software by Palisade Corporation (described earlier) Excel files for the examples in the chapters (usually two versions of each a template, or data-only version, and a finished version) Data files required for the problems and cases Excel Tutorial.xlsx, which contains a useful tutorial for getting up to speed in Excel 2007 Students who do not have a new book can purchase access to the textbook Web site at www. CengageBrain.com. Student Solutions Student Solutions to many of the odd-numbered problems (indicated in the text with a colored box on the problem number) are available in Excel format. Students can purchase access to Student Solutions files on (ISBN-10: ; ISBN-13: ). Instructor ancillaries Adopting instructors can obtain the Instructors Resource CD (IRCD) from your regional Cengage Learning Sales Representative. The IRCD includes: Problem Database.xlsx file (contains information about all problems in the book and the correspondence between them and those in the previous edition) Example files for all examples in the book, including annotated versions with additional explanations and a few extra examples that extend the examples in the book Solution files (in Excel format) for all of the problems and cases in the book and solution shells (templates) for selected problems in the modeling chapters PowerPoint presentation files for all of the examples in the book Preface xv

18 Test Bank in Word format and now also in ExamView Testing Software (new to this edition). We also extend our sincere appreciation to the reviewers who provided feedback on the authors proposed changes that resulted in this fourth edition: The book s password-protected instructor Web site, includes the above items (Test Bank in Word format only), as well as software updates, errata, additional problems and solutions, and additional resources for both students and faculty. The first author also maintains his own Web site at Acknowledgments The authors would like to thank several people who helped make this book a reality. First, the authors are indebted to Peter Kolesar, Mark Broadie, Lawrence Lapin, and William Whisler for contributing some of the excellent case studies that appear throughout the book. There are more people who helped to produce this book than we can list here. However, there are a few special people whom we were happy (and lucky) to have on our team. First, we would like to thank our editor Charles McCormick. Charles stepped into this project after two editions had already been published, but the transition has been smooth and rewarding. We appreciate his tireless efforts to make the book a continued success. We are also grateful to many of the professionals who worked behind the scenes to make this book a success: Adam Marsh, Marketing Manager; Laura Ansara, Senior Developmental Editor; Nora Heink, Editorial Assistant; Tim Bailey, Senior Content Project Manager; Stacy Shirley, Senior Art Director; and Gunjan Chandola, Senior Project Manager at MPS Limited. Henry F. Ander, Arizona State University James D. Behel, Harding University Dan Brooks, Arizona State University Robert H. Burgess, Georgia Institute of Technology George Cunningham III, Northwestern State University Rex Cutshall, Indiana University Robert M. Escudero, Pepperdine University Theodore S. Glickman, George Washington University John Gray, The Ohio State University Joe Hahn, Pepperdine University Max Peter Hoefer, Pace University Tim James, Arizona State University Teresa Jostes, Capital University Jeffrey Keisler, University of Massachusetts Boston David Kelton, University of Cincinnati Shreevardhan Lele, University of Maryland Ray Nelson, Brigham Young University William Pearce, Geneva College Thomas R. Sexton, Stony Brook University Malcolm T. Whitehead, Northwestern State University Laura A. Wilson-Gentry, University of Baltimore Jay Zagorsky, Boston University S. Christian Albright Wayne L. Winston Christopher J. Zappe May 2010 xvi Preface

19 REFERENCES Aarvik, O., and P. Randolph. The Application of Linear Programming to the Determination of Transmission Line Fees in an Electrical Power Network. Interfaces 6 (1975): Afshartous, D. Sample Size Determination for Binomial Proportion Confidence Intervals: An Alternative Perspective Motivated by a Legal Case. The American Statistician 62, no. 1 (2008): Albright, S.C. A Statistical Analysis of Hitting Streaks in Baseball. Journal of the American Statistical Association 88, no. 424 (1993): VBA for Modelers. 3rd ed. Mason, OH: South- Western Cengage Learning, Altman, E. Handbook of Corporate Finance. New York: Wiley, Appleton, D., J. French, and M. Vanderpump. Ignoring a Covariate: An Example of Simpson s Paradox. The American Statistician 50 (1996): Armstrong, S. Forecasting by Extrapolation: Conclusions from 25 Years of Research. Interfaces 14, no. 6 (1984): Long-Range Forecasting. New York: Wiley, Research on Forecasting: A Quarter-Century Review, Interfaces 16, no. 1 (1986): Arntzen, B., G. Brown, T. Harrison, and L. Trafton. Global Supply Chain Management at Digital Equipment Corporation. Interfaces 25, no. 1 (1995): Babich, P. Customer Satisfaction: How Good Is Good Enough? Quality Progress 25 (1992): Balson, W., J. Welsh, and D. Wilson. Using Decision Analysis and Risk Analysis to Manage Utility Environmental Risk. Interfaces 22, no. 6 (1992): Barnett, A. Genes, Race, IQ, and The Bell Curve. ORMS Today 22, no. 1 (1994): Bean, J., C. Noon, and G. Salton. Asset Divestiture at Homart Development Company. Interfaces 17, no. 1 (1987): , C. Noon, S. Ryan, and G. Salton. Selecting Tenants in a Shopping Mall. Interfaces 18, no. 2 (1988): Benninga, S. Numerical Methods in Finance. Cambridge, MA: MIT Press, Black, F., and M. Scholes. The Pricing of Options and Corporate Liabilities. Journal of Political Economy 81 (1973): Blyth, C. On Simpson s Paradox and the Sure-Thing Principle. Journal of the American Statistical Association 67 (1972): Borison, A. Oglethorpe Power Corporation Decides about Investing in a Major Transmission System. Interfaces 25, no. 2 (1995): Boykin, R. Optimizing Chemical Production at Monsanto. Interfaces 15, no. 1 (1985): Brigandi, A., D. Dargon, M. Sheehan, and T. Spencer. AT&T s Call Processing Simulator (CAPS) Operational Design for Inbound Call Centers. Interfaces 24, no. 1 (1994): Brinkley, P., D. Stepto, J. Haag, K. Liou, K. Wang, and W. Carr. Nortel Redefines Factory Information Technology: An OR-Driven Approach. Interfaces 28, no. 1 (1988): Brown, G., et al. Real-Time Wide Area Dispatch of Mobil Tank Trucks. Interfaces 17, no. 1 (1987): Brown, G., J. Keegan, B. Vigus, and K. Wood. The Kellogg Company Optimizes Production, Inventory, and Distribution. Interfaces 31, no. 6 (2001): Cawley, J., and P. Sommers, Voting Irregularities in the 1995 Referendum on Quebec Sovereignty. Chance 9, no. 4 (Fall 1996): Cebry, M., A. DeSilva, and F. DiLisio. Management Science in Automating Postal Operations: Facility and Equipment Planning in the United States Postal Service. Interfaces 22, no. 1 (1992): Charnes, A., and L. Cooper. Generalization of the Warehousing Model. Operational Research Quarterly 6 (1955): Cox, J., S. Ross, and M. Rubenstein. Option Pricing: A Simplified Approach. Journal of Financial Economics 7 (1979): Dantzig, G. The Diet Problem. Interfaces 20, no. 4 (1990): Deming, E., Out of the Crisis. Cambridge, MA: MIT Center for Advanced Engineering Study, DeWitt, C., L. Lasdon, A. Waren, D. Brenner, and S. Melhem. OMEGA: An Improved Gasoline Blending System for Texaco. Interfaces 19, no. 1 (1989): Duffy, T., M. Hatzakis, W. Hsu, R. Labe, B. Liao, X. Luo, J. Oh, A. Setya, and L. Yang. Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines. Interfaces 35, no. 5 (2005): Eaton, D., et al. Determining Emergency Medical Service Vehicle Deployment in Austin, Texas. Interfaces 15, no. 1 (1985):

20 Efroymson, M., and T. Ray. A Brand-Bound Algorithm for Plant Location. Operations Research 14 (1966): Engemann, K., and H. Miller. Operations Risk Management at a Major Bank. Interfaces 22, no. 6 (1992): Eppen, G., K. Martin, and L. Schrage. A Scenario Approach to Capacity Planning. Operations Research 37, no. 4 (1989): Fabian, T. A Linear Programming Model of Integrated Iron and Steel Production. Management Science 4 (1958): Feinstein, C. Deciding Whether to Test Student Athletes for Drug Use. Interfaces 20, no. 3 (1990): Fitzsimmons, J., and L. Allen. A Warehouse Location Model Helps Texas Comptroller Select Out-of-State Audit Offices. Interfaces 13, no. 5 (1983): GeneHunter. Ward Systems Group, Frederick, Maryland, Glover, F., G. Jones, D. Karney, D. Klingman, and J. Mote. An Integrated Production, Distribution, and Inventory System. Interfaces 9, no. 5 (1979): , et al. The Passenger-Mix Problem in the Scheduled Airlines. Interfaces 12 (1982): Graddy, K. Do Fast-Food Chains Price Discriminate on the Race and Income Characteristics of an Area? Journal of Business & Economic Statistics 15, no. 4 (1997): Grossman, S., and O. Hart. An Analysis of the Principal Agent Problem. Econometrica 51 (1983): Hauser, J., and S. Gaskin. Application of the Defender Consumer Model. Marketing Science 3, no. 4 (1984): Herrnstein, R., and C. Murray. The Bell Curve. New York: The Free Press, Hertz, D. Risk Analysis in Capital Investment. Harvard Business Review 42 (Jan. Feb. 1964): Hess, S. Swinging on the Branch of a Tree: Project Selection Applications. Interfaces 23, no. 6 (1993): Holmer, M. The Asset-Liability Management Strategy System at Fannie Mae. Interfaces 24, no. 3 (1994): Hoppensteadt, F., and C. Peskin. Mathematics in Medicine and the Life Sciences. New York: Springer-Verlag, Howard, R. Decision Analysis: Practice and Promise. Management Science 34, no. 6 (1988): Heathens, Heretics, and Cults: The Religious Spectrum of Decision Aiding. Interfaces 22, no. 6 (1992): Huerter, J., and W. Swart. An Integrated Labor- Management System for Taco Bell. Interfaces 28, no. 1 (1998): Jelen, B., and M. Alexander. Pivot Table Data Crunching for Microsoft Office Excel Indianapolis, IN: Que, Kaplan, R. S., and A. A. Atkinson. Advanced Management Accounting. 2nd ed. Upper Saddle River, NJ: Prentice Hall, Kauffman, J., B. Matsik, and K. Spencer. Beginning SQL Programming. Birmingham, UK: Wrox Press Ltd, Keefer, D., and S. Bodily. Three-Point Approximations for Continuous Random Variables. Management Science 29, no. 5 (1983): Kelly, J. A New Interpretation of Information Rate. Bell System Technical Journal 35 (1956): Kimes, S., and J. Fitzsimmons. Selecting Profitable Hotel Sites at La Quinta Motor Inns. Interfaces 20, no. 2 (1990): Kirkwood, C. An Overview of Methods for Applied Decision Analysis. Interfaces 22, no. 6 (1992): Klingman, D., N. Phillips, D. Steiger, and W. Young. The Successful Deployment of Management Science throughout Citgo Petroleum Corporation. Interfaces 17, no. 1 (1987): Kovar, M. Four Million Adolescents Smoke: Or Do They? Chance 13, no. 2 (2000): Krajewski, L., L. Ritzman, and P. McKenzie. Shift Scheduling in Banking Operations: A Case Application. Interfaces 10, no. 2 (1980): 1 8. Krumm, F., and C. Rolle. Management and Application of Decision and Risk Analysis in DuPont. Interfaces 22, no. 6 (1992): Lancaster, L. The Evolution of the Diet Model in Managing Food Systems. Interfaces 22, no. 5 (1992): Lanzenauer, C., E. Harbauer, B. Johnston, and D. Shuttleworth. RRSP Flood: LP to the Rescue. Interfaces 17, no. 4 (1987): LeBlanc, L., J. Hill, G. Greenwell, and A. Czesnat. Nu-kote s Spreadsheet Linear Programming Models for Optimizing Transportation. Interfaces 34, no. 2, (2004): Levitt, S., and S. Dubner. Freakonomics. New York: Harper Perennial, Levy, P., and S. Lemeshow. Sampling of Populations: Methods and Applications. 3rd ed. New York: Wiley, Littlechild, S. Marginal Pricing with Joint Costs. Economic Journal 80 (1970): Love, R., and J. Hoey. Management Science Improves Fast Food Operations. Interfaces 20, no. 2 (1990): Magoulas, K., and D. Marinos-Kouris. Gasoline Blending LP. Oil and Gas Journal (July 1988): Marcus, A. The Magellan Fund and Market Efficiency. Journal of Portfolio Management (Fall 1990): Martin, C., D. Dent, and J. Eckhart. Integrated Production, Distribution, and Inventory Planning at Libbey-Owens- Ford. Interfaces 23, no. 3 (1993): McDaniel, S., and L. Kinney. Ambush Marketing Revisited: An Experimental Study of Perceived Sponsorship Effects on Brand Awareness, Attitude Toward the Brand, and Purchase Intention. Journal of Promotion Management 3 (1996): Mellichamp, J., D. Miller, and O. Kwon. The Southern Company Uses a Probability Model for Cost Justification of Oil Sample Analysis. Interfaces 23, no. 3 (1993): References

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