Business Analytics : The Art of Modeling with Spreadsheets

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Author(s):	Powell, Stephen G.
ISBN No.:	9781119298427
Pages:	560
Year:	202409
Format:	Trade Paper
Price:	$ 218.26
Status:	Out Of Print

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PREFACE XI ABOUT THE AUTHORS XV CHAPTER 1 INTRODUCTION 1 1.1 Models and Modeling 1 1.1.1 Why Study Modeling? 2 1.1.2 Models in Business 2 1.1.3 Models in Business Education 3 1.

1.4 Benefits of Business Models 3 1.2 The Role of Spreadsheets 4 1.2.1 Risks of Spreadsheet Use 5 1.2.2 Challenges for Spreadsheet Users 6 1.2.

3 Background Knowledge for Spreadsheet Modeling 7 1.3 The Real World and the Model World 7 1.4 Lessons from Expert and Novice Modelers 9 1.4.1 Expert Modelers 9 1.4.2 Novice Modelers 11 1.5 Organization of the Book 12 1.

6 Summary 13 Suggested Readings 14 CHAPTER 2 MODELING IN A PROBLEM-SOLVING FRAMEWORK 15 2.1 Introduction 15 2.2 The Problem-Solving Process 16 2.2.1 Some Key Terms 16 2.2.2 The Six-Stage Problem-Solving Process 18 2.2.

3 Mental Models and Formal Models 23 2.3 Influence Charts 24 2.3.1 A First Example 25 2.3.2 An Income Statement as an Influence Chart 27 2.3.3 Principles for Building Influence Charts 27 2.

3.4 Two Additional Examples 28 2.4 Craft Skills for Modeling 31 2.4.1 Simplify the Problem 33 2.4.2 Break the Problem into Modules 34 2.4.

3 Build a Prototype and Refine It 35 2.4.4 Sketch Graphs of Key Relationships 38 2.4.5 Identify Parameters and Perform Sensitivity Analysis 39 2.4.6 Separate the Creation of Ideas from Their Evaluation 41 2.4.

7 Work Backward from the Desired Answer 42 2.4.8 Focus on Model Structure, not on Data Collection 43 2.5 Summary 45 Suggested Readings 46 Exercises 46 CHAPTER 3 SPREADSHEET ENGINEERING 49 3.1 Introduction 49 3.2 Designing a Spreadsheet 51 3.2.1 Sketch the Spreadsheet 51 3.

2.2 Organize the Spreadsheet into Modules 52 3.2.3 Start Small 53 3.2.4 Isolate Input Parameters 54 3.2.5 Design for Use 54 3.

2.6 Keep It Simple 54 3.2.7 Design for Communication 55 3.2.8 Document Important Data and Formulas 55 3.3 Designing a Workbook 57 3.3.

1 Use Separate Worksheets to Group Similar Kinds of Information 58 3.3.2 Design Workbooks for Ease of Navigation and Use 59 3.3.3 Design a Workbook as a Decision-Support System 60 3.4 Building a Workbook 62 3.4.1 Follow a Plan 62 3.

4.2 Build One Worksheet or Module at a Time 62 3.4.3 Predict the Outcome of Each Formula 62 3.4.4 Copy and Paste Formulas Carefully 62 3.4.5 Use Relative and Absolute Addressing to Simplify Copying 62 3.

4.6 Use the Function Wizard to Ensure Correct Syntax 63 3.4.7 Use Range Names to Make Formulas Easy to Read 63 3.4.8 Choose Input Data to Make Errors Stand Out 64 3.5 Testing a Workbook 64 3.5.

1 Check That Numerical Results Look Plausible 64 3.5.2 Check That Formulas Are Correct 65 3.5.3 Test That Model Performance Is Plausible 68 3.6 Summary 68 Suggested Readings 69 Exercises 69 CHAPTER 4 ANALYSIS USING SPREADSHEETS 71 4.1 Introduction 71 4.2 Base-case Analysis 72 4.

3 What-if Analysis 72 4.3.1 Benchmarking 73 4.3.2 Scenarios 74 4.3.3 Parametric Sensitivity 77 4.3.

4 Tornado Charts 79 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 93 5.4.1 Understand the Data 94 5.4.2 Organize and Subset the Data 94 5.4.3 Examine Individual Variables Graphically 98 5.4.

4 Calculate Summary Measures for Individual Variables 99 5.4.5 Examine Relationships among Variables Graphically 101 5.4.6 Examine Relationships among Variables Numerically 105 5.5 Data Preparation 109 5.5.1 Handling Missing Data 109 5.

5.2 Handling Errors and Outliers 111 5.5.3 Binning Continuous Data 111 5.5.4 Transforming Categorical Data 111 5.5.5 Functional Transformations 112 5.

5.6 Normalizations 113 5.6 Summary 113 Suggested Readings 114 Exercises 114 CHAPTER 6 CLASSIFICATION AND PREDICTION METHODS 117 6.1 Introduction 117 6.2 Preliminaries 117 6.2.1 The Data-Mining Process 118 6.2.

2 The Problem of Overfitting 118 6.2.3 Partitioning the Dataset 120 6.2.4 Measures of Model Quality 120 6.2.5 Variable Selection 125 6.2.

6 Setting the Cutoff in Classification 126 6.3 Classification and Prediction Trees 127 6.3.1 Classification Trees 128 6.3.2 An Application of Classification Trees 130 6.3.3 Prediction Trees 137 6.

3.4 An Application of Prediction Trees 138 6.3.5 Ensembles of Trees 141 6.4 Additional Algorithms for Classification 143 6.4.1 Logistic Regression 144 6.4.

2 Naïve Bayes 150 6.4.3 k-Nearest Neighbors 158 6.4.4 Neural Networks 162 6.5 Additional Algorithms for Prediction 169 6.5.1 Multiple Linear Regression 169 6.

5.2 k-Nearest Neighbors 177 6.5.3 Neural Networks 178 6.6 Strengths and Weaknesses of Algorithms 181 6.7 Practical Advice 182 6.8 Summary 183 Suggested Readings 184 Exercises 184 CHAPTER 7 SHORT-TERM FORECASTING 187 7.1 Introduction 187 7.

2 Forecasting with Time-Series Models 187 7.2.1 The Moving-Average Model 188 7.2.2 Measures of Forecast Accuracy 191 7.3 The Exponential Smoothing Model 192 7.4 Exponential Smoothing with a Trend 196 7.5 Exponential Smoothing with Trend and Cyclical Factors 198 7.

6 Using XLMiner for Short-Term Forecasting 202 7.7 Summary 202 Suggested Readings 203 Exercises 203 CHAPTER 8 NONLINEAR OPTIMIZATION 207 8.1 Introduction 207 8.2 An Optimization Example 208 8.2.1 Optimizing Q1 208 8.2.2 Optimization over All Four Quarters 210 8.

2.3 Incorporating the Budget Constraint 211 8.3 Building Models for Solver 213 8.3.1 Formulation 213 8.3.2 Layout 214 8.3.

3 Interpreting Results 215 8.4 Model Classification and the Nonlinear Solver 215 8.5 Nonlinear Programming Examples 217 8.5.1 Facility Location 217 8.5.2 Revenue Maximization 219 8.5.

3 Curve Fitting 221 8.5.4 Economic Order Quantity 225 8.6 Sensitivity Analysis for Nonlinear Programs 227 8.7 The Portfolio Optimization Model 231 8.8 Summary 234 Suggested Readings 234 Exercises 234 CHAPTER 9 LINEAR OPTIMIZATION 239 9.1 Introduction 239 9.1.

1 Linearity 239 9.1.2 Simplex Algorithm 240 9.2 Allocation Models 241 9.2.1 Formulation 241 9.2.2 Spreadsheet Model 242 9.

2.3 Optimization 244 9.3 Covering Models 246 9.3.1 Formulation 246 9.3.2 Spreadsheet Model 247 9.3.

3 Optimization 247 9.4 Blending Models 248 9.4.1 Blending Constraints 249 9.4.2 Formulation 251 9.4.3 Spreadsheet Model 252 9.

4.4 Optimization 252 9.5 Sensitivity Analysis for Linear Programs 253 9.5.1 Sensitivity to Objective Function Coefficients 254 9.5.2 Sensitivity to Constraint Constants 255 9.6 Patterns in Linear Programming Solutions 258 9.

6.1 Identifying Patterns 258 9.6.2 Further Examples 260 9.6.3 Review 264 9.7 Data Envelopment Analysis 265 9.8 Summary 269 Suggested Readings 270 Exercises 270 Appendix 9.

1 The Solver Sensitivity Report 274 CHAPTER 10 OPTIMIZATION OF NETWORK MODELS 277 10.1 Introduction 277 10.2 The Transportation Model 277 10.2.1 Flow Diagram 278 10.2.2 Model Formulation 278 10.2.

3 Spreadsheet Model 279 10.2.4 Optimization 280 10.2.5 Modifications to the Model 281 10.2.6 Sensitivity Analysis 282 10.3 Assignment Model 286 10.

3.1 Model Formulation 287 10.3.2 Spreadsheet Model 287 10.3.3 Optimization 288 10.3.4 Sensitivity Analysis 288 10.

4 The Transshipment Model 289 10.4.1 Formulation 290 10.4.2 Spreadsheet Model 291 10.4.3 Optimization 292 10.4.

4 Sensitivity Analysis 293 10.5 A Standard Form for Network Models 293 10.6 Network Models with Yields 295 10.6.1 Yields as Reductions in Flow 295 10.6.2 Yields as Expansions in Flow 297 10.6.

3 Patterns in General Network Models 300 10.7 Network Models for Process Technologies 301 10.7.1 Formulation 301 10.7.2 Spreadsheet Model 303 10.7.3 Optimization 304 10.

8 Summary 304 Exercises 305 CHAPTER 11 INTEGER OPTIMIZATION 309 11.1 Introduction 309 11.2 Integer Variables and the Integer Solver 310 11.3 Binary Variables and Binary Choice Models 312 11.3.1 The Capital Budgeting Problem 312 11.3.2 The Set Covering Problem 315 11.

4 Binary Variables and Logical Relationships 316 11.4.1 Relationships among Projects 317 11.4.2 Linking Constraints and Fixed Costs 319 11.4.3 Threshold Levels and Quantity Discounts 323 11.5 The Facility Location Model 324 11.

5.1 The Capacitated Problem 325 11.5.2 The Uncapacitated Problem 327 11.5.3 The Assortment Model 329 11.6 Summary 330 Suggested Readings 331 Exercises 331 CHAPTER 12 OPTIMIZATION OF NONSMOOTH MODELS 335 12.1 Introduction 335

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