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Now in its fifth edition, Powell and Baker’s Business Analytics: The Art of Modeling with Spreadsheets provides students and business analysts with the technical knowledge and skill needed to develop real expertise in business modeling. In this book, the authors cover spreadsheet engineering, management science, and the modeling craft. The briefness & accessibility of this title offers opportunities to integrate other materials –such as cases -into the course. It can be used in any number of courses or departments where modeling is a key skill.
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PREFACE IX

CHAPTER 1 INTRODUCTION 1

1.1 Models and Modeling 1

1.3 The Real World and the Model World 7

1.4 Lessons from Expert and Novice Modelers 9

1.5 Organization of the Book 12

1.6 Summary 13

CHAPTER 2 MODELING IN A PROBLEM-SOLVING FRAMEWORK 15

2.1 Introduction 15

2.2 The Problem-Solving Process 16

2.3 Influence Charts 24

2.4 Craft Skills for Modeling 31

2.5 Summary 45

Exercises 46

3.1 Introduction 49

3.3 Designing a Workbook 57

3.4 Building a Workbook 62

3.5 Testing a Workbook 64

3.6 Summary 68

Exercises 69

CHAPTER 4 ANALYSIS USING SPREADSHEETS 71

4.1 Introduction 71

4.2 Base-Case Analysis 72

4.3 What-If Analysis 72

4.4 Breakeven Analysis 81

4.5 Optimization Analysis 83

4.6 Simulation and Risk Analysis 84

4.7 Summary 85

Exercises 85

CHAPTER 5 DATA EXPLORATION AND PREPARATION 89

5.1 Introduction 89

5.2 Dataset Structure 90

5.3 Types of Data 93

5.4 Data Exploration 94

5.5 Data Preparation 111

5.6 Summary 110

Exercises 110

CHAPTER 6 CLASSIFICATION AND PREDICTION METHODS 117

6.1 Introduction 117

6.2 Preliminaries 117

6.3 Classification and Prediction Trees 134

6.4 Additional Algorithms for Classification 128

6.5 Additional Algorithms for Prediction 134

6.6 Strengths and Weaknesses of Algorithms 134

6.8 Summary 164

Exercises 164

CHAPTER 7 SHORT-TERM FORECASTING 167

7.1 Introduction 167

7.2 Forecasting with Time-Series Models 167

7.3 The Exponential Smoothing Model 172

7.4 Exponential Smoothing with a Trend 176

7.5 Exponential Smoothing with Trend and Cyclical Factors 178

7.6 Using XLMiner for Short-Term Forecasting 182

7.7 Summary 183

Exercises 183

CHAPTER 8 NONLINEAR OPTIMIZATION 187

8.1 Introduction 187

8.2 An Optimization Example 188

8.3 Building Models for Solver 193

8.4 Model Classification and the Nonlinear Solver 195

8.5 Nonlinear Programming Examples 197

8.6 Sensitivity Analysis for Nonlinear Programs 207

8.7 The Portfolio Optimization Model 211

8.8 Summary 214

Exercises 214

CHAPTER 9 LINEAR OPTIMIZATION 219

9.1 Introduction 219

9.2 Allocation Models 221

9.3 Covering Models 226

9.4 Blending Models 228

9.5 Sensitivity Analysis for Linear Programs 233

9.6 Patterns in Linear Programming Solutions 238

9.7 Data Envelopment Analysis 245

9.8 Summary 249

Exercises 250

Appendix 9.1 The Solver Sensitivity Report 254

CHAPTER 10 OPTIMIZATION OF NETWORK MODELS 257

10.1 Introduction 257

10.2 The Transportation Model 257

10.3 Assignment Model 266

10.4 The Transshipment Model 269

10.5 A Standard Form for Network Models 273

10.6 Network Models with Yields 275

10.7 Network Models for Process Technologies 281

10.8 Summary 284

Exercises 285

CHAPTER 11 INTEGER OPTIMIZATION 289

11.1 Introduction 289

11.2 Integer Variables and the Integer Solver 290

11.3 Binary Variables and Binary Choice Models 292

11.4 Binary Variables and Logical Relationships 296

11.5 The Facility Location Model 304

11.6 Summary 310

Exercises 311

CHAPTER 12 OPTIMIZATION OF NONSMOOTH MODELS 315

12.1 Introduction 315

12.2 Features of the Evolutionary Solver 315

12.3 Curve Fitting (Revisited) 317

12.4 The Advertising Budget Problem (Revisited) 319

12.5 The Capital Budgeting Problem (Revisited) 322

12.6 The Fixed Cost Problem (Revisited) 324

12.7 The Machine-Sequencing Problem 325

12.8 The Traveling Salesperson Problem 328

12.9 Group Assignment 330

12.10 Summary 332

Exercises 332

CHAPTER 13 DECISION ANALYSIS 337

13.1 Introduction 337

13.2 Payoff Tables and Decision Criteria 338

13.3 Using Trees to Model Decisions 341

13.4 Using Decision Tree Software 349

13.5 Maximizing Expected Utility with Decision Tree 355

13.6 Summary 358

Exercises 358

CHAPTER 14 MONTE CARLO SIMULATION 363

14.1 Introduction 363

14.2 A Simple Illustration 364

14.3 The Simulation Process 366

14.4 Corporate Valuation Using Simulation 375

14.5 Option Pricing Using Simulation 384

14.6 Selecting Uncertain Parameters 391

14.7 Selecting Probability Distributions 393

14.8 Ensuring Precision in Outputs 400

14.9 Interpreting Simulation Outcomes 404

14.11 Summary 408

Exercises 409

CHAPTER 15 OPTIMIZATION IN SIMULATION 415

15.1 Introduction 415

15.2 Optimization with One or Two Decision Variables 415

15.3 Stochastic Optimization 422

15.4 Chance Constraints 428

15.5 Two-Stage Problems with Recourse 433

15.6 Summary 437

Exercises 438

MODELING CASES 443

APPENDIX 1 BASIC EXCEL SKILLS 459

Introduction 459

Excel Prerequisites 459

The Excel Window 460

Configuring Excel 462

Manipulating Windows and Sheets 463

Selecting Cells 465

Entering Text and Data 465

Editing Cells 466

Formatting 467

Basic Formulas 468

Basic Functions 469

Charting 473

Printing 475

Help Options 476

Keyboard Shortcuts 477

Naming Cells and Ranges 479

APPENDIX 2 MACROS AND VBA 487

Introduction 487

Recording a Macro 487

Editing a Macro 490

Creating a User-Defined Function 492

References 494

APPENDIX 3 BASIC PROBABILITY CONCEPTS 495

Introduction 495

Probability Distributions 495

Examples of Discrete Distributions 498

Examples of Continuous Distributions 499

Expected Values 501

Cumulative Distribution Functions 502

Tail Probabilities 503

Variability 504

Sampling 505

INDEX 509

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New To This Edition

• NEW coverage of data exploration and data mining, shifting focus from explanatory modeling to predictive modeling, and the modern emphasis on predictive performance using validation data.
• UPDATED content reflecting the latest version of Analytic Solver Platform throughout the textbook.
• EXPANDED coverage of data analysis.
• REVISED - Streamlined approach to  data exploration and incorporated data preparation directly into the text (Chapter 5)
• REVISED - Substantial revision and reorganization of Chapter 6 (Classification and Prediction Methods), by presenting one algorithm (CART) and showing how it can be used for both data mining tasks.

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• Access to spreadsheet files for all the models presented in the text. Incorporated the latest versions of Excel & Analytic Solver Platform for Education, an integrated software platform for sensitivity analysis, optimization, decision trees, data exploration and mining, and simulation.
• Focus on three skill areas that a business analyst needs to become an effective modeler: spreadsheet engineering, management science, and modeling craft.

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Instructors Resources
Wiley Instructor Companion Site
Test Bank
Test your students' comprehension with this printable, editable digital collection of fill-in-the-blank, multiple-choice, true/false, and free-response questions.
PowerPoint Presentations
Our PowerPoint presentations contain a combination of key concepts allowing you to illustrate important topics with images, figures, and problems from the textbook.
Text Figures - Excel files
Case and Exercise Solutions
Data Files - Excel files
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Students Resources
Wiley Student Companion Site
PowerPoint Presentations
Our PowerPoint presentations contain a combination of key concepts allowing you to illustrate important topics with images, figures, and problems from the textbook.
Text Figures - Excel files
Data Files - Excel files
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