Ap worksheet 03g quantitative analysis answers

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Overview of Applications in the Book, by Discipline

Accounting Accounts receivable 285, 297 Auditing for price errors 329 Developing a flexible budget 537 Estimating total tax refunds 325 Estimating total taxable income 325 Overhead cost analysis 423, 437, 471, 490, 520, 524

Economics/Government Demand and cost for electricity 461 Demand for desktops and laptops 402 Demand for French bread 481 Demand for heating oil 536 Demand for microwaves 182 Electricity pricing 736 Home and condo prices 78 Housing price structure 480 Presidential elections 19 Sales of new houses 566, 572

Finance Bond investment strategy 893 Capital budgeting 714 Cash management 852 DJIA index 58, 77 Investing for college 892 Investing for retirement 481, 857 Investment strategy 703 Investor’s after-tax profit 181 Land purchase decision 274 Liquidity risk management 829 Market return scenarios 152, 157 Mutual fund returns 171, 195 New car development 847 Pension fund management 708 Portfolio analysis 743 Random walk of stock prices 562 Stock hedging 757

Human Resources Employee empowerment 389 Employee retention 361 Gender discrimination 450, 457, 514 Jobs in statistics and mathematics 897 Personnel testing 178 Productivity due to exercise 384

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Marketing Catalog marketing 503, 508 Churn in cellular phone market 136 Clustering shoe customers 934 Customer complaints 349, 378 Customer valuation 865 DVD movie renters 310 Electronics sales 108 Frozen lasagna dinner buyers 125, 915, 919, 923 Furniture pricing 480 Marketing and selling condos 873 New pizza style decisions 365, 373 New product decisions 233, 240, 243, 260 Olympics sponsors 363 Response to new sandwich 319, 346, 348 Running shoe style decisions 274 Sales presentation ratings 339 Sales response to coupons 343 Sales versus promotions 421, 433 Soft-drink can style decisions 380 Supermarket sales 197 Supermarket transactions 27 Value of free maintenance agreement 868

Miscellaneous Statistical Crime in the U.S. 54 Cruise ship entertainment 16 Election returns 200 Family income sampling 283 Forecasting U.S. population 557 IQ and bell curve 166 Predictors of successful movies 79, 482 Questionnaire responses 23 Relationship between smoking and drinking 82 Removing Vioxx from market 412 Sample size determination in legal case 279 Saving, spending, social climbing 136 Simpson’s paradox 165 University admissions 360

Operations Management Aggregate planning 693 Airline boarding strategies 759 Airline hub location decisions 729 Arrivals at bank 135 Automobile production 755 Battery lifetimes 191 Bidding for contracts 831 Blending oil 670 Developing Army helicopter component 276

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Developing electronic timing system 275 Delivery times at restaurant 361 Distribution of metal strip widths 396 Employee scheduling 663 Expensive watch production 219 Forecasting sales 551, 554, 559, 566, 572, 576, 581, 586 Inventory management 208 Learning curve for production 466 Manufacturing plastics operations 599 Ordering decisions 781, 784, 796, 806, 812, 815 Out-of-spec products 350 Overbooking at airlines 198 Product mix decisions 603, 631, 721 Production quantity decisions 827, 828 Production scheduling 641, 840 Production, inventory, distribution decisions 661 Quality control at paper company 179 Reliability of motors 336 Site selection of motor inns 417 Timing uncertainty in construction 144 Transportation, logistics decisions 677, 686 Variability in machine parts 333 Warranty costs 835

Sports/Gaming Baseball salaries 31, 40, 46, 49, 88 Games at McDonald’s 139 Golf stats on PGA tour 95 NCAA basketball tournament simulation 882 Revenue management at casino 539 Streak shooting in basketball 201 Wheel of fortune simulation 300 Winning at craps 879 Winning the lottery 220

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Australia • Brazil • Mexico • Singapore • United Kingdom • United States

Business Analytics: Data Analysis and Decision Making

6th Edition

S. Christian Albright Kelly School of Business, Indiana University, Emeritus

Wayne L. Winston Kelly School of Business, Indiana University

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Business Analytics: Data Analysis & Decision Making, Sixth Edition

S. Christian Albright and Wayne L. Winston

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To my wonderful wife Mary—my best friend and travel mate; to Sam, Lindsay, Teddy, and Archie; and to Bryn, our ball-playing Welsh corgi!Archie; and to Bryn, our ball-playing Welsh corgi! S.C.A

To my wonderful familyTo my wonderful family W.L.W.

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S. Christian Albright got his B.S. degree in Mathematics from Stanford in 1968 and his PhD in Operations Research from Stanford in 1972. He taught in the Operations & Decision Technologies Department in the Kelley School of Business at Indiana University (IU) for close to 40 years, before retiring from teaching in 2011. While at IU, he taught courses in management science, computer simulation, statistics, and computer programming to all levels of business students, including undergraduates, MBAs, and doctoral students. In addition,

he taught simulation modeling at General Motors and Whirlpool, and he taught database analysis for the Army. He 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 worked for several years after “retirement” with the Palisade Corporation developing training materials for its software products, he has developed a commercial version of his Excel® tutorial, called ExcelNow!, and he continues to revise his textbooks.

On the personal side, Chris has been married for 44 years to his wonderful wife, Mary, who retired several years ago after teaching 7th grade English for 30 years. They have one son, Sam, who lives in Philadelphia with his wife Lindsay and their two sons, Teddy and Archer. 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 real passion is for playing real classical piano music.

Wayne L. Winston taught in the Operations & Decision Technologies Department in the Kelley School of Business at Indiana University for close to 40 before retiring a few years ago. Wayne received his B.S. degree in Mathematics from MIT and his PhD in Operations Research from Yale. He has written the successful textbooks Operations Research: Applications and Algorithms, Mathematical Programming: Applications and Algorithms, Simulation Modeling Using @RISK, Practical Management Science, Data Analysis and Decision Making, Financial Models

Using Simulation and Optimization, and Mathletics. Wayne has published more than 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. He has taught classes at Microsoft, GM, Ford, Eli Lilly, Bristol-Myers Squibb, Arthur Andersen, Roche, PricewaterhouseCoopers, and NCR, and in “retirement,” he is currently teaching several courses at the

About the Authors

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v

University of Houston. 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.

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vi

Brief Contents

Preface xviii 1 Introduction to Business Analytics 1

Part 1 Exploring Data 17 2 Describing the Distribution of a Single Variable 19 3 Finding Relationships among Variables 79

Part 2 Probability and Decision Making Under Uncertainty 137

4 Probability and Probability Distributions 139 5 Normal, Binomial, Poisson, and Exponential Distributions 166 6 Decision Making under Uncertainty 222

Part 3 Statistical Inference 277 7 Sampling and Sampling Distributions 279 8 Confidence Interval Estimation 311 9 Hypothesis Testing 363

Part 4 Regression Analysis and Time Series Forecasting 415 10 Regression Analysis: Estimating Relationships 417 11 Regression Analysis: Statistical Inference 482 12 Time Series Analysis and Forecasting 539

Part 5 Optimization and Simulation Modeling 597 13 Introduction to Optimization Modeling 599 14 Optimization Models 661 15 Introduction to Simulation Modeling 759 16 Simulation Models 829

Part 6 Advanced Data Analysis 895 17 Data Mining 897

Introduction to Spreadsheet Modeling (only in MindTap)

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Brief Contents vii

Part 7 Bonus Online Material* 18-1 18 Importing Data into Excel 18-3 19 Analysis of Variance and Experimental Design 19-1 20 Statistical Process Control 20-1 Appendix A Statistical Reporting A-1

•Bonus Online Material for this text can be found on the text companion website at cengagebrain.com.

References 943 Index 945

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viii

Contents

Preface xviii

1 Introduction to Business Analytics 1 1-1 Introduction 3 1-2 Overview of the Book 4

1-2a The Methods 4 1-2b The Software 7

1-3 Modeling and Models 10 1-3a Graphical Models 10 1-3b Algebraic Models 11 1-3c Spreadsheet Models 12 1-3d A Seven-Step Modeling Process 13

1-4 Conclusion 15

PART 1 EXPLORING DATA 17

2 Describing the Distribution of a Single Variable 19 2-1 Introduction 21 2-2 Basic Concepts 22

2-2a Populations and Samples 22 2-2b Data Sets, Variables, and Observations 23 2-2c Types of Data 24

2-3 Descriptive Measures for Categorical Variables 26 2-4 Descriptive Measures for Numerical Variables 30

2-4a Numerical Summary Measures 31 2-4b Numerical Summary Measures with StatTools 40 2-4c Analysis ToolPak Add-In 45 2-4d Charts for Numerical Variables 45

2-5 Time Series Data 54 2-6 Outliers and Missing Values 61

2-6a Outliers 61 2-6b Missing Values 61

2-7 Excel Tables for Filtering, Sorting, and Summarizing 63 2-8 Conclusion 71

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Contents ix

3 Finding Relationships among Variables 79 3-1 Introduction 80 3-2 Relationships among Categorical Variables 82 3-3 Relationships among Categorical Variables and a Numerical Variable 86

3-3a Stacked and Unstacked Formats 86 3-4 Relationships among Numerical Variables 95

3-4a Scatterplots 95 3-4b Correlation and Covariance 101

3-5 Pivot Tables 108 3-6 Conclusion 131

PART 2 PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY 137

4 Probability and Probability Distributions 139 4-1 Introduction 140 4-2 Probability Essentials 142

4-2a Rule of Complements 142 4-2b Addition Rule 142 4-2c Conditional Probability and the Multiplication Rule 143 4-2d Probabilistic Independence 146 4-2e Equally Likely Events 147 4-2f Subjective Versus Objective Probabilities 147

4-3 Probability Distribution of a Single Random Variable 150 4-3a Summary Measures of a Probability Distribution 151 4-3b Conditional Mean and Variance 154

4-4 Introduction to Simulation 156 4-5 Conclusion 160

5 Normal, Binomial, Poisson, and Exponential Distributions 166 5-1 Introduction 167 5-2 The Normal Distribution 168

5-2a Continuous Distributions and Density Functions 168 5-2b The Normal Density 169 5-2c Standardizing: Z-ValuesZ-ValuesZ 170 5-2d Normal Tables and Z-ValuesZ-ValuesZ 172 5-2e Normal Calculations in Excel 174 5-2f5-2f Empirical Rules Revisited 177 5-2g Weighted Sums of Normal Random Variables 177

5-3 Applications of the Normal Distribution 178

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x Contents

5-4 The Binomial Distribution 190 5-4a Mean and Standard Deviation of the Binomial Distribution 193 5-4b The Binomial Distribution in the Context of Sampling 193 5-4c The Normal Approximation to the Binomial 194

5-5 Applications of the Binomial Distribution 195 5-6 The Poisson and Exponential Distributions 207

5-6a The Poisson Distribution 207 5-6b The Exponential Distribution 210

5-7 Conclusion 212

6 Decision Making under Uncertainty 222 6-1 Introduction 223 6-2 Elements of Decision Analysis 225

6-2a Identifying the Problem 225 6-2b Possible Decisions 226 6-2c Possible Outcomes 226 6-2d Probabilities of Outcomes 226 6-2e Payoffs and Costs 227 6-2f6-2f Decision Criterion 227 6-2g More about the EMV Criterion 228 6-2h Decision Trees 230

6-3 One-Stage Decision Problems 232 6-4 The PrecisionTree Add-In 236 6-5 Multistage Decision Problems 239 6-6 The Role of Risk Aversion 257

6-6a Utility Functions 258 6-6b Exponential Utility 259 6-6c Certainty Equivalents 262 6-6d Is Expected Utility Maximization Used? 263

6-7 Conclusion 264

PART 3 STATISTICAL INFERENCE 277

7 Sampling and Sampling Distributions 279 7-1 Introduction 280 7-2 Sampling Terminology 280 7-3 Methods for Selecting Random Samples 282

7-3a Simple Random Sampling 282 7-3b Systematic Sampling 287 7-3c Stratified Sampling 288 7-3d Cluster Sampling 289 7-3e Multistage Sampling Schemes 290

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Contents xi

7-4 Introduction to Estimation 292 7-4a Sources of Estimation Error 292 7-4b Key Terms in Sampling 293 7-4c Sampling Distribution of the Sample Mean 295 7-4d The Central Limit Theorem 299 7-4e Sample Size Selection 304 7-4f7-4f Summary of Key Ideas for Simple Random Sampling 305

7-5 Conclusion 307

8 Confidence Interval Estimation 311 8-1 Introduction 312 8-2 Sampling Distributions 314

8-2a The t Distributiont Distributiont 314 8-2b Other Sampling Distributions 317

8-3 Confidence Interval for a Mean 317 8-4 Confidence Interval for a Total 324 8-5 Confidence Interval for a Proportion 326 8-6 Confidence Interval for a Standard Deviation 331 8-7 Confidence Interval for the Difference between Means 335

8-7a Independent Samples 335 8-7b Paired Samples 339

8-8 Confidence Interval for the Difference between Proportions 342 8-9 Sample Size Selection 344

8-9a Sample Size Selection for Estimation of the Mean 345 8-9b Sample Size Selection for Estimation of Other Parameters 347

8-10 Conclusion 352

9 Hypothesis Testing 363 9-1 Introduction 364 9-2 Concepts in Hypothesis Testing 365

9-2a Null and Alternative Hypotheses 366 9-2b One-Tailed Versus Two-Tailed Tests 366 9-2c Types of Errors 367 9-2d Significance Level and Rejection Region 368 9-2e Significance from p-valuesp-valuesp 368 9-2f Type II Errors and Power 370 9-2g Hypothesis Tests and Confidence Intervals 371 9-2h Practical versus Statistical Significance 371

9-3 Hypothesis Tests for a Population Mean 372 9-4 Hypothesis Tests for Other Parameters 377

9-4a Hypothesis Tests for a Population Proportion 377 9-4b Hypothesis Tests for Differences between Population Means 379

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xii Contents

9-4c Hypothesis Test for Equal Population Variances 387 9-4d Hypothesis Tests for Differences between Population Proportions 388

9-5 Tests for Normality 395 9-6 Chi-Square Test for Independence 401 9-7 Conclusion 406

PART 4 REGRESSION ANALYSIS AND TIME SERIES FORECASTING 415

10 Regression Analysis: Estimating Relationships 417 10-1 Introduction 418 10-2 Scatterplots: Graphing Relationships 421

10-2a Linear versus Nonlinear Relationships 426 10-2b Outliers 426 10-2c Unequal Variance 427 10-2d No Relationship 427

10-3 Correlations: Indicators of Linear Relationships 428 10-4 Simple Linear Regression 430

10-4a Least Squares Estimation 430 10-4b Standard Error of Estimate 438 10-4c The Percentage of Variation Explained: R-Square 440

10-5 Multiple Regression 443 10-5a Interpretation of Regression Coefficients 443 10-5b Interpretation of Standard Error of Estimate and R-Square 446

10-6 Modeling Possibilities 449 10-6a Dummy Variables 450 10-6b Interaction Variables 456 10-6c Nonlinear Transformations 460

10-7 Validation of the Fit 470 10-8 Conclusion 472

11 Regression Analysis: Statistical Inference 482 11-1 Introduction 484 11-2 The Statistical Model 484 11-3 Inferences about the Regression Coefficients 488

11-3a Sampling Distribution of the Regression Coefficients 489 11-3b Hypothesis Tests for the Regression Coefficients and p-Valuesp-Valuesp 491 11-3c A Test for the Overall Fit: The ANOVA Table 492

11-4 Multicollinearity 496 11-5 Include/Exclude Decisions 502 11-6 Stepwise Regression 507 11-7 Outliers 512

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Contents xiii

11-8 Violations of Regression Assumptions 517 11-8a Nonconstant Error Variance 517 11-8b Nonnormality of Residuals 518 11-8c Autocorrelated Residuals 519

11-9 Prediction 521 11-10 Conclusion 527

12 Time Series Analysis and Forecasting 539 12-1 Introduction 540 12-2 Forecasting Methods: An Overview 541

12-2a Extrapolation Models 541 12-2b Econometric Models 542 12-2c Combining Forecasts 543 12-2d Components of Time Series Data 543 12-2e Measures of Accuracy 546

12-3 Testing for Randomness 548 12-3a The Runs Test 550 12-3b Autocorrelation 552

12-4 Regression-Based Trend Models 556 12-4a Linear Trend 556 12-4b Exponential Trend 559

12-5 The Random Walk Model 562 12-6 Moving Averages Forecasts 565 12-7 Exponential Smoothing Forecasts 570

12-7a Simple Exponential Smoothing 571 12-7b Holt’s Model for Trend 575

12-8 Seasonal Models 580 12-8a Winters’ Exponential Smoothing Model 581 12-8b Deseasonalizing: The Ratio-to-Moving-Averages Method 584 12-8c Estimating Seasonality with Regression 585

12-9 Conclusion 590

PART 5 OPTIMIZATION AND SIMULATION MODELING 597

13 Introduction to Optimization Modeling 599 13-1 Introduction 600 13-2 Introduction to Optimization 601 13-3 A Two-Variable Product Mix Model 602 13-4 Sensitivity Analysis 615

13-4a Solver’s Sensitivity Report 616 13-4b SolverTable Add-In 619 13-4c Comparison of Solver’s Sensitivity Report and SolverTable 626

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xiv Contents

13-5 Properties of Linear Models 626 13-6 Infeasibility and Unboundedness 629 13-7 A Larger Product Mix Model 631 13-8 A Multiperiod Production Model 640 13-9 A Comparison of Algebraic and Spreadsheet Models 649 13-10 A Decision Support System 750 13-11 Conclusion 652

14 Optimization Models 661 14-1 Introduction 662 14-2 Employee Scheduling Models 663 14-3 Blending Models 670 14-4 Logistics Models 676

14-4a Transportation Models 677 14-4b Other Logistics Models 685

14-5 Aggregate Planning Models 693 14-6 Financial Models 703 14-7 Integer Optimization Models 714

14-7a Capital Budgeting Models 714 14-7b Fixed-Cost Models 720 14-7c Set-Covering Models 729

14-8 Nonlinear Optimization Models 735 14-8a Basic Ideas of Nonlinear Optimization 735 14-8b Managerial Economics Models 736 14-8c Portfolio Optimization Models 740

14-9 Conclusion 749

15 Introduction to Simulation Modeling 759 15-1 Introduction 760 15-2 Probability Distributions for Input Variables 762

15-2a Types of Probability Distributions 763 15-2b Common Probability Distributions 766 15-2c Using @RISK to Explore Probability Distributions 770

15-3 Simulation and the Flaw of Averages 780 15-4 Simulation with Built-in Excel Tools 783 15-5 Introduction to @RISK 794

15-5a @RISK Features 795 15-5b Loading @RISK 795 15-5c @RISK Models with a Single Random Input Variable 796 15-5d Some Limitations of @RISK 806 15-5e @RISK Models with Several Random Input Variables 806

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Contents xv

15-6 The Effects of Input Distributions on Results 811 15-6a Effect of the Shape of the Input Distribution(s) 812 15-6b Effect of Correlated Input Variables 815

15-7 Conclusion 820

16 Simulation Models 829 16-1 Introduction 831 16-2 Operations Models 831

16-2a Bidding for Contracts 831 16-2b Warranty Costs 835 16-2c Drug Production with Uncertain Yield 840

16-3 Financial Models 847 16-3a Financial Planning Models 847 16-3b Cash Balance Models 852 16-3c Investment Models 857

16-4 Marketing Models 864 16-4a Models of Customer Loyalty 864 16-4b Marketing and Sales Models 872

16-5 Simulating Games of Chance 879 16-5a Simulating the Game of Craps 879 16-5b Simulating the NCAA Basketball Tournament 882

16-6 Conclusion 885

PART 6 ADVANCED DATA ANALYSIS 895

17 Data Mining 897 17-1 Introduction 898 17-2 Data Exploration and Visualization 900

17-2a Introduction to Relational Databases 900 17-2b Online Analytical Processing (OLAP) 901 17-2c Power Pivot and Self-Service BI Tools in Excel 904 17-2d Visualization Software 911

17-3 Classification Methods 912 17-3a Logistic Regression 913 17-3b Neural Networks 918 17-3c Naïve Bayes 923 17-3d Classification Trees 926 17-3e Measures of Classification Accuracy 927 17-3f17-3f Classification with Rare Events 930

17-4 Clustering 933 17-5 Conclusion 938

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xvi Contents

PART 7 BONUS ONLINE MATERIAL 18-1

18 Importing Data into Excel 18-3 18-1 Introduction 18-4 18-2 Rearranging Excel Data 18-5 18-3 Importing Text Data 18-9 18-4 Importing Data into Excel 18-15

18-4a Importing from Access with Old Tools 18-15 18-4b Importing from Access with Power Query 18-16 18-4c Using Microsoft Query 18-18 18-4d SQL Statements and M 18-26 18-4e Web Queries 18-26

18-5 Cleansing Data 18-28 18-6 Conclusion 18-35

19 Analysis of Variance and Experimental Design 19-1 19-1 Introduction 19-2 19-2 One-Way ANOVA 19-5

19-2a The Equal-Means Test 19-5 19-2b Confidence Intervals for Differences between Means 19-8 19-2c Using a Logarithmic Transformation 19-11

19-3 Using Regression to Perform ANOVA 19-17 19-4 The Multiple Comparison Problem 19-20 19-5 Two-Way ANOVA 19-24

19-5a Confidence Intervals for Contrasts 19-31 19-5b Assumptions of Two-Way ANOVA 19-34

19-6 More about Experimental Design 19-35 19-6a Randomization 19-36 19-6b Blocking 19-38 19-6c Incomplete Designs 19-42

19-7 Conclusion 19-45

20 Statistical Process Control 20-1 20-1 Introduction 20-3 20-2 Deming’s 14 Points 20-4 20-3 Introduction to Control Charts 20-7 20-4 Control Charts for Variables 20-9

20-4a Control Charts and Hypothesis Testing 20-15 20-4b Other Out-of-Control Indications 20-16 20-4c Rational Subsamples 20-17 20-4d Deming’s Funnel Experiment and Tampering 20-20 20-4e Control Charts in the Service Industry 20-23

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Contents xvii

20-5 Control Charts for Attributes 20-27 20-5a The p Chartp Chartp 20-27 20-5b The Red Bead Experiment 20-31

20-6 Process Capability 20-34 20-6a Process Capability Indexes 20-37 20-6b More on Motorola and 6-sigma 20-42

20-7 Conclusion 20-45

Appendix A: Statistical Reporting A-1 A-1 Introduction A-1 A-2 Suggestions for Good Statistical Reporting A-2

A-2a Planning A-2 A-2b Developing a Report A-3 A-2c Be Clear A-4 A-2d Be Concise A-5 A-2e Be Precise A-5

A-3 Examples of Statistical Reports A-7 A-4 Conclusion A-18

References 943

Index 945

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xviii

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 �rst 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 not majoring in not a quantitative area. They are typically business majors in �nance, 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 soft- ware—speci�cally, Microsoft Excel®. This is a package that most students already know and will almost surely use in their careers. Our MBA students at Indiana University have been so turned on by the required course that is based on this book that almost all of them almost all of them almost all (mostly �nance and marketing majors) have taken 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

Business Analytics: Data Analysis and Decision Making is different from the many �ne textbooks written for statistics and management science. Our rationale for writing this book is based on four fundamental objectives.

■ Integrated coverage and applications. The book provides a uni�ed approach to business-related problems by integrating methods and applications that have been tra- ditionally taught in separate courses, speci�cally statistics and management science.

■ 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.

■ Spreadsheet-based teaching. 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.

■ Latest tools. This is not a static �eld. The software keeps changing, and even the mathematical algorithms behind the software continue to evolve. Each edition of this book has presented the most recent tools in Excel and the accompanying Excel add- ins, and the current edition is no exception.

Preface

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Preface xix

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. A 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 some advanced data analysis methods. Essentially, 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 re�ection 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 to 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, prob- lems, and cases that deal with portfolio optimization, workforce scheduling, market share analysis, capital budgeting, new product analysis, and many others.

Practical in Approach

This book has been designed 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 standard deviation as a measure of variability, 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 numeri- cal outputs in a matter of seconds, freeing the rest of the time for in-depth interpretation of the results, 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 we let the software perform the number crunching.

Spreadsheet-based Teaching

We are strongly committed to teaching spreadsheet-based, example-driven courses, regard- less 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 �rst to move to spreadsheet-based teaching about two decades ago, and we have never regretted the move.

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xx Preface

What We Hope to Accomplish in This Book

Condensing the ideas in the previous paragraphs, we hope to:

■ continue to make quantitative courses attractive to a wide audience by making these topics real, accessible, and interesting;

■ give students plenty 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; and

■ develop spreadsheet skills, including experience with powerful spreadsheet add-ins, that add immediate value to students’ other courses and for their future careers.

New in the Sixth Edition

There are several important changes in this edition.

■ MindTap: Offered for the �rst time with this text, MindTap is a customizable digital course solution that includes an interactive eBook, auto-graded exercises from the textbook, author-created videos, �ashcards, and more. MindTap includes all videos in support of the text, using StatTools or using JMP, as well as Excel solutions �les for students to use for checking selected problems from the text (odd-numbered ques- tions). MindTap also includes a chapter on Spreadsheet Modeling, which is not found in the print text, as an additional resource for faculty and students. For more informa- tion on MindTap, as well as ordering options, please contact your Cengage Learning consultant.

■ Focus on Excel 2016: The newest version of Excel was released just in time for this book’s revision, so all of the explanations and screenshots are based on this newest version. Except for cosmetic changes in the user interface, you will see almost no changes, and if you are still using Excel 2013 or a previous version, you shouldn’t have any problems following along with this book. However, Excel 2016 does have some nice features that are included here, including histograms, box plots, and the “power” tools discussed in Chapters 17 and 18.

■ Revised Chapter 6: The chapter on decision making under uncertainty has been totally rewritten. Now, a single “new product decisions” example is developed and extended throughout the chapter to promote continuity.

■ BigPicture diagrams: In the optimization and simulation chapters, it has always been dif�cult for students to go from a verbal description of a problem to an eventual spreadsheet model. In this edition, we include “big picture” diagrams of the models that will hopefully act as a bridge from the verbal descriptions to the spreadsheet models. These diagrams have been created from the latest add-in in the Palisade DecisionTools Suite, the BigPicture add-in. Users of the book have access to BigPicture, just like @RISK and the other Palisade add-ins.

■ Somewhat less reliance on StatTools: Although we continue to rely on the StatTools add-in for much of the statistical number crunching, especially in the regression and time series chapters, we rely on Excel formulas for the “easier” material in the con�- dence interval and hypothesis testing chapters, where Excel’s functions are perfectly adequate and might even be more insightful. Nevertheless, we include many brief videos that walk you through the StatTools procedures. These videos can be found within the MindTap product that accompanies this text.

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Preface xxi

■ Inclusion of JMP: The book continues to use Excel and Excel add-ins as the pri- mary tools for data analysis. However, the student edition of the statistical software package JMP from SAS can be bundled with the text for minimal cost for users of our book. We do not try to replicate JMP’s �ne and extensive online help for learn- ing the software, but for many of the statistical examples in the book, we provide short videos showing how JMP can generate the results from Excel or StatTools. These videos can be found within the MindTap product that is available for this text. For ordering information on how to include JMP student edition with the book, please contact your Cengage learning consultant.

■ Updated videos: As in the �fth edition, the materials for the book include over 50 videos, particularly for the models in the optimization and simulation chapters. These videos have been redone (and shortened). These videos can be found within the MindTap product that accompanies this text.

■ Updated Problems: As in previous editions, there are some new and some updated problems. Again, we have included a �le, essentially a database of problems, which is available to instructors. This �le, DADM 6e Problem Database.xlsx, indicates the context of each of the problems, and it also shows the correspondence between problems in this edition and those in the previous edition.

■ More Data Mining Content: Chapter 17 on data mining has been expanded. First, there is more coverage on Excel’s newest “power” tools, especially Power Pivot, which are now included with Excel 2016 (at least in versions Professional Plus and higher). Second, there are detailed sections on the Naïve Bayes method for classi�ca- tion and an Excel-only method for clustering.

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 casual users into casual power users who can take full advantage of its feapower users who can take full advantage of its feapower - tures. If you learn no more than this, you will be acquiring 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® Suite Add-in

The textbook website for Business Analytics: Data Analysis and Decision Making provides a link to the powerful DecisionTools® Suite by Palisade Corporation. (The version avail- able is compatible with Excel 2016 and previous versions of Excel.) This suite includes seven separate add-ins:

■ @RISK, 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

■ NeuralTools®, an add-in for estimating complex, nonlinear relationships

■ EvolverTM, an add-in for performing optimization (an alternative to Excel’s Solver)

■ BigPicture, a very smart drawing add-in, useful for depicting model relationships

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xxii Preface

We use @RISK and PrecisionTree extensively in the chapters on simulation and decision making under uncertainty, and we use StatTools extensively in the data analysis chapters. We also use BigPicture in the optimization and simulation chapters to provide a “bridge” between a problem statement and an eventual spreadsheet model.

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 hun- dreds 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.1

SolverTable Add-in

We also include SolverTable, a supplement to Excel’s built-in Solver for optimization.2 If you have ever had dif�culty 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 extenoptimal output values. SolverTable is used extenoptimal - sively in the optimization chapters.

Windows versus Mac

In our own courses, we have seen an increasing number of students using Macintosh lap- tops rather than Windows laptops. Fortunately, this is not a problem, and our students not a problem, and our students not have followed along �ne with their Macs. However, these students should be advised to use a Windows emulation program (Bootcamp or Parallels are good candidates), along with Of�ce for Windows. Be aware that a few Excel features discussed in the book, such as pivot charts and Power Pivot, are not yet supported by Excel 2016 for the Mac, and the not yet supported by Excel 2016 for the Mac, and the not SolverTable and Palisade add-ins will not work in Excel 2016 for the Mac.not work in Excel 2016 for the Mac.not

Potential Course Structures

Although we have used the book for our own required one-semester course, there is admit- tedly much more material 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.

■ One-semester Required Course, with No Statistics Prerequisite (or where MBA students need a refresher for whatever statistics they learned previously): If data analysis is the primary focus of the course, then Chapters 2–5, 7–11, and possibly Chapter 17 should be covered. Depending on the time remaining, any of the topics in Chapters 6 (decision making under uncertainty), 12 (time series analysis), 13–14 (optimization), or 15–16 (simulation) can be covered in practically any order.

■ One-semester Required Course, with a Statistics Prerequisite: Assuming that students know the basic elements of statistics (up through hypothesis testing), the

1 Visit www.kelley.iu.edu/albrightbooks for specific details on these limitations. 2 Although SolverTable is available on this textbook’s website, it is also available for free from Albright’s website, www.kelley.iu.edu/albrightbooks.

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Preface xxiii

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. The instructor can then choose among any of the topics in Chapters 6, 10–11, 12, 13–14, or 15–16 (in practically any order), or to �ll the remainder of the course.

■ Two-semester Required Sequence: Given the luxury of spreading the topics over two semesters, the entire book, or at least most of it, can be covered. The statistics topics in Chapters 2–5 and 7–9 should be covered in chronological order before other statistical topics (regression and time series analysis), but the remaining chapters can be covered in practically any order.

Custom Publishing

Cengage Learning is dedicated to making the educational experience unique for all learn- ers by creating custom materials that best suit your course needs. With Cengage Learning you can create a custom solution where you have the ability to choose your book’s content, length, sequence, even the cover design. You may combine content from multiple Cengage Learning titles and add other materials, including your own original work, to create your ideal customized text. If you would like to learn more about our custom publishing ser- vices, please contact your Cengage Learning representative3 or visit us at www.cengage .com/custom.

Instructor Supplements

Textbook Website: cengage.com/login

The companion website provides immediate access to an array of teaching resources— including data and solutions �les for all of the Examples, Problems, and Cases in the book, Chapters 18–20 and Appendix A, Test Bank �les, PowerPoint slides, and access to the DecisionTools® Suite by Palisade Corporation and the SolverTable add-in. You can easily download the instructor resources you need from the password-protected, instructor-only section of the site.

Test Bank

Cengage Learning Testing Powered by Cognero is a �exible, online system that allows you to:

■ author, edit, and manage test bank content from multiple Cengage Learning solutions

■ create multiple test versions in an instant

■ deliver tests from your LMS, your classroom, or wherever you want

3 Find your Learning Consultant at sites.cengage.com/repfinder.

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xxiv Preface

Student Supplements

Textbook Website: www.cengagebrain.com

Every new student edition of this book comes with access to the Business Analytics: Data Analysis and Decision Making, 6e textbook website that links to the following �les and tools:

■ Excel �les for the examples in the chapters (usually two versions of each— a template, or data-only version, and a �nished version)

■ Data �les required for the Problems and Cases

■ Excel Tutorial for Windows.xlsx, which contains a useful tutorial for getting up to speed in Excel (Excel Tutorial for the Mac.xlsx is also available)

■ Chapters 18–20 and Appendix A

■ DecisionTools® Suite software by Palisade Corporation (described earlier)

■ SolverTable add-in

The resources listed above can be accessed through our MindTap learning system. For additional free resources go to www.cengagebrain .com, search by ISBN 9781305947542, click on the “Free Materials” tab, and select “Access Now.” The resources you need will be listed both per chapter (by selecting a chapter from the drop-down list) and for the entire book (under Book Resources).

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Preface xxv

Acknowledgements

We are also grateful to many of the professionals who worked behind the scenes to make this book a success: Mike Schenk, Product Director; Sr. Product Team Manager, Joe Sabatino; Product Manager, Aaron Arnsparger; Associate Content Developer, Brad Sullender; Product Project Manager, Kailash Rawat; Marketing Manager, Nate Anderson, Marketing Coordinator, Eileen Corcoran; and Product Assistant, Audrey Jacobs.

We also extend our sincere appreciation to the reviewers who provided feedback on the authors’ proposed changes that resulted in this sixth edition:

John Aloysius, Walton College of Business, University of Arkansas

Henry F. Ander, Arizona State University

Dr. Baabak Ashuri, School of Building Construction, Georgia Institute of Technology

James Behel, Harding University

Robert H. Burgess, Scheller College of Business, Georgia Institute of Technology

Paul Damien, McCombs School of Business, University of Texas in Austin

Parviz Ghandforoush, Virginia Tech

Betsy Greenberg, University of Texas

Anissa Harris, Harding University

Tim James, Arizona State University

Norman Johnson, C.T. Bauer College of Business, University of Houston

Shivraj Kanungo, The George Washington University

Miguel Lejeune, The George Washington University

José Lobo, Arizona State University

Stuart Low, Arizona State University

Lance Matheson, Virginia Tech

Patrick R. McMullen, Wake Forest University

Barbara A. Price, PhD, Georgia Southern University

Laura Wilson-Gentry, University of Baltimore

Toshiyuki Yuasa, University of Houston

S. Christian Albright

Wayne L. Winston

December 2015

6th Edition

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1

BUSINESS ANALYTICS PROVIDES INSIGHTS AND IMPROVES PERFORMANCE

This book is all about using quantitative modeling to help companies make better decisions and improve performance. We have been teaching management science for decades, and companies have been using the management science methods discussed in this book for decades to improve performance and save millions of dollars. Indeed, the applied journal Interfaces, discussed later in this chapter, has chronicled management science success stories for years. Therefore, we were a bit surprised when a brand new term, Business Analytics (BA), became hugely popular several years ago. All of a sudden, BA promised to be the road to success. By using quantitative BA methods—data analysis, optimization, simulation, prediction, and others—companies could drastically improve business performance. Haven’t those of us in management science been doing this for years? What is different about BA that has made it so popular, both in the academic world and even more so in the business world?

The truth is that BA does use the same quantitative methods that have been the hallmark of management science for years, the same methods you will learn in this book. BA has not all of a sudden invented brand new quanti- tative methods to eclipse traditional management science methods. The main difference is that BA uses big data to solve business problems and provide insights. Companies now have access to huge sources of data, and the technology is now available to use huge data sets for statistical and quantitative analysis, predictive modeling, optimization, and simulation. In short, the same quantitative methods that have been available for years can now be even more effective by utilizing big data and the corresponding technology.

Introduction to Business Analytics

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2 Chapter 1 Introduction to Business Analytics

For a quick introduction to BA, you should visit the BA Wikipedia site (search the Web for “business analytics”). Among other things, it lists areas where BA plays a prominent role, including the following: retail sales analytics; financial services analyt- ics; risk and credit analytics; marketing analytics; pricing analytics; supply chain analytics; and transportation analytics. If you glance through the examples and problems in this book, you will see that most of them come from these same areas. Again, the differ-book, you will see that most of them come from these same areas. Again, the differ-book, you will see that most of them come from these same areas. Again, the differ ence is that we use relatively small data sets to get you started—we do not want to overwhelm you with gigabytes of data—whereas real applications of BA use huge data sets to advantage.

A more extensive discussion of BA can be found in the Fall 2011 research report, Analytics: The Widening Divide, published in the MIT Sloan Management Review in collabo- ration with IBM, a key developer of BA software (search the Web for the article’s title). This 22-page article discusses what BA is and provides several case studies. In addition, it lists three key competencies people need to compete successfully in the BA world—and hopefully you will be one of these people.

■ Competency 1: Information management skills to manage the data. This competency involves expertise in a variety of techniques for managing data. Given the key role of data in BA methods, data quality is extremely important. With data com- ing from a number of disparate sources, both internal and external to an organization, achieving data quality is no small feat.

■ Competency 2: Analytics skills and tools to understand the data. We were not surprised, but rather very happy, to see this competency listed among the requirements because these skills are exactly the skills we cover throughout this book—optimization with advanced quantitative algorithms, simulation, and others.

■ Competency 3: Data-oriented culture to act on the data. This refers to the culture within the organization. Everyone involved, especially top management, must believe strongly in fact-based decisions arrived at using analytical methods.

The article argues persuasively that the companies that have these competencies and have embraced BA have a distinct competitive advantage over companies that are just starting to use BA methods or are not using them at all. This explains the title of the article. The gap between companies that embrace BA and those that do not will only widen in the future.

One final note about the relationship between BA and management science is that, at the time this book was being revised (Winter 2014), a special issue of the journal Management Science was about to be published. The entire focus of this special issue is on BA. The following is an excerpt from the Call for Papers for this issue (search the Web for “management science business analytics special issue”).

“We envision business analytics applied to many domains, including, but surely not limited to: digital market design and operation; network and social-graph analysis; pricing and revenue management; targeted marketing and customer relationship management; fraud and security; sports and entertainment; retailing to healthcare to financial services to many other industries. We seek novel modeling and empirical work which includes, among others, probability modeling, structural empirical models, and/or optimization methods.”

This is even more confirmation of the tight relationship between BA and manage- ment science. As you study this book, you will see examples of most of the topics listed in this quote. ■

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1-1 Introduction 3

1-1 INTRODUCTION We are living in the age of technology. This has two important implications for everyone enter- ing the business world. First, technology has made it possible to collect huge amounts of data. Retailers collect point-of-sale data on products and customers every time a transaction occurs; credit agencies have all sorts of data on people who have or would like to obtain credit; invest- ment companies have a limitless supply of data on the historical patterns of stocks, bonds, and other securities; and government agencies have data on economic trends, the environment, social welfare, consumer product safety, and virtually everything else imaginable. It has become relatively easy to collect the data. As a result, data are plentiful. However, as many organizations easy to collect the data. As a result, data are plentiful. However, as many organizations easy have discovered, it is quite a challenge to make sense of all the data they have collected.

A second important implication of technology is that it has given many more people the power and responsibility to analyze data and make decisions on the basis of quantitative analy- sis. People entering the business world can no longer pass all of the quantitative analysis to the “quant jocks,” the technical specialists who have traditionally done the number crunching. The vast majority of employees now have a desktop or laptop computer at their disposal, access to relevant data, and training in easy-to-use software, particularly spreadsheet and database soft- ware. For these employees, statistics and other quantitative methods are no longer forgotten top- ics they once learned in college. Quantitative analysis is now an integral part of their daily jobs.

A large amount of data already exists, and it will only increase in the future. Many com- panies already complain of swimming in a sea of data. However, enlightened companies are seeing this expansion as a source of competitive advantage. In fact, one of the hottest topics in today’s business world is business analytics, also called data analytics. These terms have been created to encompass all of the types of analysis discussed in this book, so they aren’t really new; we have been teaching them for years. The new aspect of business analytics is that it typically implies the analysis of very large data sets, the kind that companies currently encounter. (For this reason, the term big data has also become popular.) By using quantitative methods to uncover the information in these data sets and then acting on this information— again guided by quantitative analysis—companies are able to gain advantages that their less enlightened competitors are not able to gain. Here are several pertinent examples.

■ Direct marketers analyze enormous customer databases to see which customers are likely to respond to various products and types of promotions. Marketers can then target different classes of customers in different ways to maximize pro�ts—and give their customers what they want.

■ Hotels and airlines also analyze enormous customer databases to see what their custom- ers want and are willing to pay for. By doing this, they have been able to devise very clever pricing strategies, where different customers pay different prices for the same accommodations. For example, a business traveler typically makes a plane reservation closer to the time of travel than a vacationer. The airlines know this. Therefore, they reserve seats for these business travelers and charge them a higher price for the same seats. The airlines pro�t from clever pricing strategies, and the customers are happy.