{"product_id":"business-analytics-isbn-9781119298427","title":"Business Analytics","description":"Now in its fifth edition, Powell and Baker’s \u003cb\u003e\u003ci\u003eBusiness Analytics: The Art of Modeling with Spreadsheets\u003c\/i\u003e\u003c\/b\u003e 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 \u0026amp; 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. \u003cp\u003ePREFACE XI\u003c\/p\u003e \u003cp\u003eABOUT THE AUTHORS XV\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1 INTRODUCTION 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Models and Modeling 1\u003c\/p\u003e \u003cp\u003e1.1.1 Why Study Modeling? 2\u003c\/p\u003e \u003cp\u003e1.1.2 Models in Business 2\u003c\/p\u003e \u003cp\u003e1.1.3 Models in Business Education 3\u003c\/p\u003e \u003cp\u003e1.1.4 Benefits of Business Models 3\u003c\/p\u003e \u003cp\u003e1.2 The Role of Spreadsheets 4\u003c\/p\u003e \u003cp\u003e1.2.1 Risks of Spreadsheet Use 5\u003c\/p\u003e \u003cp\u003e1.2.2 Challenges for Spreadsheet Users 6\u003c\/p\u003e \u003cp\u003e1.2.3 Background Knowledge for Spreadsheet Modeling 7\u003c\/p\u003e \u003cp\u003e1.3 The Real World and the Model World 7\u003c\/p\u003e \u003cp\u003e1.4 Lessons from Expert and Novice Modelers 9\u003c\/p\u003e \u003cp\u003e1.4.1 Expert Modelers 9\u003c\/p\u003e \u003cp\u003e1.4.2 Novice Modelers 11\u003c\/p\u003e \u003cp\u003e1.5 Organization of the Book 12\u003c\/p\u003e \u003cp\u003e1.6 Summary 13\u003c\/p\u003e \u003cp\u003eSuggested Readings 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2 MODELING IN A PROBLEM-SOLVING FRAMEWORK 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 The Problem-Solving Process 16\u003c\/p\u003e \u003cp\u003e2.2.1 Some Key Terms 16\u003c\/p\u003e \u003cp\u003e2.2.2 The Six-Stage Problem-Solving Process 18\u003c\/p\u003e \u003cp\u003e2.2.3 Mental Models and Formal Models 23\u003c\/p\u003e \u003cp\u003e2.3 Influence Charts 24\u003c\/p\u003e \u003cp\u003e2.3.1 A First Example 25\u003c\/p\u003e \u003cp\u003e2.3.2 An Income Statement as an Influence Chart 27\u003c\/p\u003e \u003cp\u003e2.3.3 Principles for Building Influence Charts 27\u003c\/p\u003e \u003cp\u003e2.3.4 Two Additional Examples 28\u003c\/p\u003e \u003cp\u003e2.4 Craft Skills for Modeling 31\u003c\/p\u003e \u003cp\u003e2.4.1 Simplify the Problem 33\u003c\/p\u003e \u003cp\u003e2.4.2 Break the Problem into Modules 34\u003c\/p\u003e \u003cp\u003e2.4.3 Build a Prototype and Refine It 35\u003c\/p\u003e \u003cp\u003e2.4.4 Sketch Graphs of Key Relationships 38\u003c\/p\u003e \u003cp\u003e2.4.5 Identify Parameters and Perform Sensitivity Analysis 39\u003c\/p\u003e \u003cp\u003e2.4.6 Separate the Creation of Ideas from Their Evaluation 41\u003c\/p\u003e \u003cp\u003e2.4.7 Work Backward from the Desired Answer 42\u003c\/p\u003e \u003cp\u003e2.4.8 Focus on Model Structure, not on Data Collection 43\u003c\/p\u003e \u003cp\u003e2.5 Summary 45\u003c\/p\u003e \u003cp\u003eSuggested Readings 46\u003c\/p\u003e \u003cp\u003eExercises 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3 SPREADSHEET ENGINEERING 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.2 Designing a Spreadsheet 51\u003c\/p\u003e \u003cp\u003e3.2.1 Sketch the Spreadsheet 51\u003c\/p\u003e \u003cp\u003e3.2.2 Organize the Spreadsheet into Modules 52\u003c\/p\u003e \u003cp\u003e3.2.3 Start Small 53\u003c\/p\u003e \u003cp\u003e3.2.4 Isolate Input Parameters 54\u003c\/p\u003e \u003cp\u003e3.2.5 Design for Use 54\u003c\/p\u003e \u003cp\u003e3.2.6 Keep It Simple 54\u003c\/p\u003e \u003cp\u003e3.2.7 Design for Communication 55\u003c\/p\u003e \u003cp\u003e3.2.8 Document Important Data and Formulas 55\u003c\/p\u003e \u003cp\u003e3.3 Designing a Workbook 57\u003c\/p\u003e \u003cp\u003e3.3.1 Use Separate Worksheets to Group Similar Kinds of Information 58\u003c\/p\u003e \u003cp\u003e3.3.2 Design Workbooks for Ease of Navigation and Use 59\u003c\/p\u003e \u003cp\u003e3.3.3 Design a Workbook as a Decision-Support System 60\u003c\/p\u003e \u003cp\u003e3.4 Building a Workbook 62\u003c\/p\u003e \u003cp\u003e3.4.1 Follow a Plan 62\u003c\/p\u003e \u003cp\u003e3.4.2 Build One Worksheet or Module at a Time 62\u003c\/p\u003e \u003cp\u003e3.4.3 Predict the Outcome of Each Formula 62\u003c\/p\u003e \u003cp\u003e3.4.4 Copy and Paste Formulas Carefully 62\u003c\/p\u003e \u003cp\u003e3.4.5 Use Relative and Absolute Addressing to Simplify Copying 62\u003c\/p\u003e \u003cp\u003e3.4.6 Use the Function Wizard to Ensure Correct Syntax 63\u003c\/p\u003e \u003cp\u003e3.4.7 Use Range Names to Make Formulas Easy to Read 63\u003c\/p\u003e \u003cp\u003e3.4.8 Choose Input Data to Make Errors Stand Out 64\u003c\/p\u003e \u003cp\u003e3.5 Testing a Workbook 64\u003c\/p\u003e \u003cp\u003e3.5.1 Check That Numerical Results Look Plausible 64\u003c\/p\u003e \u003cp\u003e3.5.2 Check That Formulas Are Correct 65\u003c\/p\u003e \u003cp\u003e3.5.3 Test That Model Performance Is Plausible 68\u003c\/p\u003e \u003cp\u003e3.6 Summary 68\u003c\/p\u003e \u003cp\u003eSuggested Readings 69\u003c\/p\u003e \u003cp\u003eExercises 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4 ANALYSIS USING SPREADSHEETS 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e4.2 Base-case Analysis 72\u003c\/p\u003e \u003cp\u003e4.3 What-if Analysis 72\u003c\/p\u003e \u003cp\u003e4.3.1 Benchmarking 73\u003c\/p\u003e \u003cp\u003e4.3.2 Scenarios 74\u003c\/p\u003e \u003cp\u003e4.3.3 Parametric Sensitivity 77\u003c\/p\u003e \u003cp\u003e4.3.4 Tornado Charts 79\u003c\/p\u003e \u003cp\u003e4.4 Breakeven Analysis 81\u003c\/p\u003e \u003cp\u003e4.5 Optimization Analysis 83\u003c\/p\u003e \u003cp\u003e4.6 Simulation and Risk Analysis 84\u003c\/p\u003e \u003cp\u003e4.7 Summary 85\u003c\/p\u003e \u003cp\u003eExercises 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5 DATA EXPLORATION AND PREPARATION 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.2 Dataset Structure 90\u003c\/p\u003e \u003cp\u003e5.3 Types of Data 93\u003c\/p\u003e \u003cp\u003e5.4 Data Exploration 93\u003c\/p\u003e \u003cp\u003e5.4.1 Understand the Data 94\u003c\/p\u003e \u003cp\u003e5.4.2 Organize and Subset the Data 94\u003c\/p\u003e \u003cp\u003e5.4.3 Examine Individual Variables Graphically 98\u003c\/p\u003e \u003cp\u003e5.4.4 Calculate Summary Measures for Individual Variables 99\u003c\/p\u003e \u003cp\u003e5.4.5 Examine Relationships among Variables Graphically 101\u003c\/p\u003e \u003cp\u003e5.4.6 Examine Relationships among Variables Numerically 105\u003c\/p\u003e \u003cp\u003e5.5 Data Preparation 109\u003c\/p\u003e \u003cp\u003e5.5.1 Handling Missing Data 109\u003c\/p\u003e \u003cp\u003e5.5.2 Handling Errors and Outliers 111\u003c\/p\u003e \u003cp\u003e5.5.3 Binning Continuous Data 111\u003c\/p\u003e \u003cp\u003e5.5.4 Transforming Categorical Data 111\u003c\/p\u003e \u003cp\u003e5.5.5 Functional Transformations 112\u003c\/p\u003e \u003cp\u003e5.5.6 Normalizations 113\u003c\/p\u003e \u003cp\u003e5.6 Summary 113\u003c\/p\u003e \u003cp\u003eSuggested Readings 114\u003c\/p\u003e \u003cp\u003eExercises 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6 CLASSIFICATION AND PREDICTION METHODS 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 117\u003c\/p\u003e \u003cp\u003e6.2 Preliminaries 117\u003c\/p\u003e \u003cp\u003e6.2.1 The Data-Mining Process 118\u003c\/p\u003e \u003cp\u003e6.2.2 The Problem of Overfitting 118\u003c\/p\u003e \u003cp\u003e6.2.3 Partitioning the Dataset 120\u003c\/p\u003e \u003cp\u003e6.2.4 Measures of Model Quality 120\u003c\/p\u003e \u003cp\u003e6.2.5 Variable Selection 125\u003c\/p\u003e \u003cp\u003e6.2.6 Setting the Cutoff in Classification 126\u003c\/p\u003e \u003cp\u003e6.3 Classification and Prediction Trees 127\u003c\/p\u003e \u003cp\u003e6.3.1 Classification Trees 128\u003c\/p\u003e \u003cp\u003e6.3.2 An Application of Classification Trees 130\u003c\/p\u003e \u003cp\u003e6.3.3 Prediction Trees 137\u003c\/p\u003e \u003cp\u003e6.3.4 An Application of Prediction Trees 138\u003c\/p\u003e \u003cp\u003e6.3.5 Ensembles of Trees 141\u003c\/p\u003e \u003cp\u003e6.4 Additional Algorithms for Classification 143\u003c\/p\u003e \u003cp\u003e6.4.1 Logistic Regression 144\u003c\/p\u003e \u003cp\u003e6.4.2 Naïve Bayes 150\u003c\/p\u003e \u003cp\u003e6.4.3 k-Nearest Neighbors 158\u003c\/p\u003e \u003cp\u003e6.4.4 Neural Networks 162\u003c\/p\u003e \u003cp\u003e6.5 Additional Algorithms for Prediction 169\u003c\/p\u003e \u003cp\u003e6.5.1 Multiple Linear Regression 169\u003c\/p\u003e \u003cp\u003e6.5.2 k-Nearest Neighbors 177\u003c\/p\u003e \u003cp\u003e6.5.3 Neural Networks 178\u003c\/p\u003e \u003cp\u003e6.6 Strengths and Weaknesses of Algorithms 181\u003c\/p\u003e \u003cp\u003e6.7 Practical Advice 182\u003c\/p\u003e \u003cp\u003e6.8 Summary 183\u003c\/p\u003e \u003cp\u003eSuggested Readings 184\u003c\/p\u003e \u003cp\u003eExercises 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7 SHORT-TERM FORECASTING 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 187\u003c\/p\u003e \u003cp\u003e7.2 Forecasting with Time-Series Models 187\u003c\/p\u003e \u003cp\u003e7.2.1 The Moving-Average Model 188\u003c\/p\u003e \u003cp\u003e7.2.2 Measures of Forecast Accuracy 191\u003c\/p\u003e \u003cp\u003e7.3 The Exponential Smoothing Model 192\u003c\/p\u003e \u003cp\u003e7.4 Exponential Smoothing with a Trend 196\u003c\/p\u003e \u003cp\u003e7.5 Exponential Smoothing with Trend and Cyclical Factors 198\u003c\/p\u003e \u003cp\u003e7.6 Using XLMiner for Short-Term Forecasting 202\u003c\/p\u003e \u003cp\u003e7.7 Summary 202\u003c\/p\u003e \u003cp\u003eSuggested Readings 203\u003c\/p\u003e \u003cp\u003eExercises 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8 NONLINEAR OPTIMIZATION 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 207\u003c\/p\u003e \u003cp\u003e8.2 An Optimization Example 208\u003c\/p\u003e \u003cp\u003e8.2.1 Optimizing Q1 208\u003c\/p\u003e \u003cp\u003e8.2.2 Optimization over All Four Quarters 210\u003c\/p\u003e \u003cp\u003e8.2.3 Incorporating the Budget Constraint 211\u003c\/p\u003e \u003cp\u003e8.3 Building Models for Solver 213\u003c\/p\u003e \u003cp\u003e8.3.1 Formulation 213\u003c\/p\u003e \u003cp\u003e8.3.2 Layout 214\u003c\/p\u003e \u003cp\u003e8.3.3 Interpreting Results 215\u003c\/p\u003e \u003cp\u003e8.4 Model Classification and the Nonlinear Solver 215\u003c\/p\u003e \u003cp\u003e8.5 Nonlinear Programming Examples 217\u003c\/p\u003e \u003cp\u003e8.5.1 Facility Location 217\u003c\/p\u003e \u003cp\u003e8.5.2 Revenue Maximization 219\u003c\/p\u003e \u003cp\u003e8.5.3 Curve Fitting 221\u003c\/p\u003e \u003cp\u003e8.5.4 Economic Order Quantity 225\u003c\/p\u003e \u003cp\u003e8.6 Sensitivity Analysis for Nonlinear Programs 227\u003c\/p\u003e \u003cp\u003e8.7 The Portfolio Optimization Model 231\u003c\/p\u003e \u003cp\u003e8.8 Summary 234\u003c\/p\u003e \u003cp\u003eSuggested Readings 234\u003c\/p\u003e \u003cp\u003eExercises 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9 LINEAR OPTIMIZATION 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 239\u003c\/p\u003e \u003cp\u003e9.1.1 Linearity 239\u003c\/p\u003e \u003cp\u003e9.1.2 Simplex Algorithm 240\u003c\/p\u003e \u003cp\u003e9.2 Allocation Models 241\u003c\/p\u003e \u003cp\u003e9.2.1 Formulation 241\u003c\/p\u003e \u003cp\u003e9.2.2 Spreadsheet Model 242\u003c\/p\u003e \u003cp\u003e9.2.3 Optimization 244\u003c\/p\u003e \u003cp\u003e9.3 Covering Models 246\u003c\/p\u003e \u003cp\u003e9.3.1 Formulation 246\u003c\/p\u003e \u003cp\u003e9.3.2 Spreadsheet Model 247\u003c\/p\u003e \u003cp\u003e9.3.3 Optimization 247\u003c\/p\u003e \u003cp\u003e9.4 Blending Models 248\u003c\/p\u003e \u003cp\u003e9.4.1 Blending Constraints 249\u003c\/p\u003e \u003cp\u003e9.4.2 Formulation 251\u003c\/p\u003e \u003cp\u003e9.4.3 Spreadsheet Model 252\u003c\/p\u003e \u003cp\u003e9.4.4 Optimization 252\u003c\/p\u003e \u003cp\u003e9.5 Sensitivity Analysis for Linear Programs 253\u003c\/p\u003e \u003cp\u003e9.5.1 Sensitivity to Objective Function Coefficients 254\u003c\/p\u003e \u003cp\u003e9.5.2 Sensitivity to Constraint Constants 255\u003c\/p\u003e \u003cp\u003e9.6 Patterns in Linear Programming Solutions 258\u003c\/p\u003e \u003cp\u003e9.6.1 Identifying Patterns 258\u003c\/p\u003e \u003cp\u003e9.6.2 Further Examples 260\u003c\/p\u003e \u003cp\u003e9.6.3 Review 264\u003c\/p\u003e \u003cp\u003e9.7 Data Envelopment Analysis 265\u003c\/p\u003e \u003cp\u003e9.8 Summary 269\u003c\/p\u003e \u003cp\u003eSuggested Readings 270\u003c\/p\u003e \u003cp\u003eExercises 270\u003c\/p\u003e \u003cp\u003eAppendix 9.1 The Solver Sensitivity Report 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 10 OPTIMIZATION OF NETWORK MODELS 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 277\u003c\/p\u003e \u003cp\u003e10.2 The Transportation Model 277\u003c\/p\u003e \u003cp\u003e10.2.1 Flow Diagram 278\u003c\/p\u003e \u003cp\u003e10.2.2 Model Formulation 278\u003c\/p\u003e \u003cp\u003e10.2.3 Spreadsheet Model 279\u003c\/p\u003e \u003cp\u003e10.2.4 Optimization 280\u003c\/p\u003e \u003cp\u003e10.2.5 Modifications to the Model 281\u003c\/p\u003e \u003cp\u003e10.2.6 Sensitivity Analysis 282\u003c\/p\u003e \u003cp\u003e10.3 Assignment Model 286\u003c\/p\u003e \u003cp\u003e10.3.1 Model Formulation 287\u003c\/p\u003e \u003cp\u003e10.3.2 Spreadsheet Model 287\u003c\/p\u003e \u003cp\u003e10.3.3 Optimization 288\u003c\/p\u003e \u003cp\u003e10.3.4 Sensitivity Analysis 288\u003c\/p\u003e \u003cp\u003e10.4 The Transshipment Model 289\u003c\/p\u003e \u003cp\u003e10.4.1 Formulation 290\u003c\/p\u003e \u003cp\u003e10.4.2 Spreadsheet Model 291\u003c\/p\u003e \u003cp\u003e10.4.3 Optimization 292\u003c\/p\u003e \u003cp\u003e10.4.4 Sensitivity Analysis 293\u003c\/p\u003e \u003cp\u003e10.5 A Standard Form for Network Models 293\u003c\/p\u003e \u003cp\u003e10.6 Network Models with Yields 295\u003c\/p\u003e \u003cp\u003e10.6.1 Yields as Reductions in Flow 295\u003c\/p\u003e \u003cp\u003e10.6.2 Yields as Expansions in Flow 297\u003c\/p\u003e \u003cp\u003e10.6.3 Patterns in General Network Models 300\u003c\/p\u003e \u003cp\u003e10.7 Network Models for Process Technologies 301\u003c\/p\u003e \u003cp\u003e10.7.1 Formulation 301\u003c\/p\u003e \u003cp\u003e10.7.2 Spreadsheet Model 303\u003c\/p\u003e \u003cp\u003e10.7.3 Optimization 304\u003c\/p\u003e \u003cp\u003e10.8 Summary 304\u003c\/p\u003e \u003cp\u003eExercises 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 11 INTEGER OPTIMIZATION 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 309\u003c\/p\u003e \u003cp\u003e11.2 Integer Variables and the Integer Solver 310\u003c\/p\u003e \u003cp\u003e11.3 Binary Variables and Binary Choice Models 312\u003c\/p\u003e \u003cp\u003e11.3.1 The Capital Budgeting Problem 312\u003c\/p\u003e \u003cp\u003e11.3.2 The Set Covering Problem 315\u003c\/p\u003e \u003cp\u003e11.4 Binary Variables and Logical Relationships 316\u003c\/p\u003e \u003cp\u003e11.4.1 Relationships among Projects 317\u003c\/p\u003e \u003cp\u003e11.4.2 Linking Constraints and Fixed Costs 319\u003c\/p\u003e \u003cp\u003e11.4.3 Threshold Levels and Quantity Discounts 323\u003c\/p\u003e \u003cp\u003e11.5 The Facility Location Model 324\u003c\/p\u003e \u003cp\u003e11.5.1 The Capacitated Problem 325\u003c\/p\u003e \u003cp\u003e11.5.2 The Uncapacitated Problem 327\u003c\/p\u003e \u003cp\u003e11.5.3 The Assortment Model 329\u003c\/p\u003e \u003cp\u003e11.6 Summary 330\u003c\/p\u003e \u003cp\u003eSuggested Readings 331\u003c\/p\u003e \u003cp\u003eExercises 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 12 OPTIMIZATION OF NONSMOOTH MODELS 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 335\u003c\/p\u003e \u003cp\u003e12.2 Features of the Evolutionary Solver 335\u003c\/p\u003e \u003cp\u003e12.3 Curve Fitting (Revisited) 338\u003c\/p\u003e \u003cp\u003e12.4 The Advertising Budget Problem (Revisited) 339\u003c\/p\u003e \u003cp\u003e12.5 The Capital Budgeting Problem (Revisited) 342\u003c\/p\u003e \u003cp\u003e12.6 The Fixed Cost Problem (Revisited) 344\u003c\/p\u003e \u003cp\u003e12.7 The Machine-Sequencing Problem 345\u003c\/p\u003e \u003cp\u003e12.8 The Traveling Salesperson Problem 347\u003c\/p\u003e \u003cp\u003e12.9 Group Assignment 350\u003c\/p\u003e \u003cp\u003e12.10 Summary 352\u003c\/p\u003e \u003cp\u003eExercises 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 13 DECISION ANALYSIS 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 357\u003c\/p\u003e \u003cp\u003e13.2 Payoff Tables and Decision Criteria 358\u003c\/p\u003e \u003cp\u003e13.2.1 Benchmark Criteria 358\u003c\/p\u003e \u003cp\u003e13.2.2 Incorporating Probabilities 359\u003c\/p\u003e \u003cp\u003e13.3 Using Trees to Model Decisions 361\u003c\/p\u003e \u003cp\u003e13.3.1 Decision Trees 362\u003c\/p\u003e \u003cp\u003e13.3.2 Decision Trees for a Series of Decisions 364\u003c\/p\u003e \u003cp\u003e13.3.3 Principles for Building and Analyzing Decision Trees 367\u003c\/p\u003e \u003cp\u003e13.3.4 The Cost of Uncertainty 368\u003c\/p\u003e \u003cp\u003e13.4 Using Decision Tree Software 369\u003c\/p\u003e \u003cp\u003e13.4.1 Solving a Simple Example with Decision Tree 370\u003c\/p\u003e \u003cp\u003e13.4.2 Sensitivity Analysis with Decision Tree 371\u003c\/p\u003e \u003cp\u003e13.4.3 Minimizing Expected Cost with Decision Tree 373\u003c\/p\u003e \u003cp\u003e13.5 Maximizing Expected Utility with Decision Tree 375\u003c\/p\u003e \u003cp\u003e13.6 Summary 378\u003c\/p\u003e \u003cp\u003eSuggested Readings 378\u003c\/p\u003e \u003cp\u003eExercises 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 14 MONTE CARLO SIMULATION 383\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 383\u003c\/p\u003e \u003cp\u003e14.2 A Simple Illustration 384\u003c\/p\u003e \u003cp\u003e14.3 The Simulation Process 386\u003c\/p\u003e \u003cp\u003e14.3.1 Base-Case Model 387\u003c\/p\u003e \u003cp\u003e14.3.2 Sensitivity Analysis 388\u003c\/p\u003e \u003cp\u003e14.3.3 Specifying Probability Distributions 390\u003c\/p\u003e \u003cp\u003e14.3.4 Specifying Outputs 391\u003c\/p\u003e \u003cp\u003e14.3.5 Setting Simulation Parameters 391\u003c\/p\u003e \u003cp\u003e14.3.6 Analyzing Simulation Outputs 391\u003c\/p\u003e \u003cp\u003e14.4 Corporate Valuation Using Simulation 395\u003c\/p\u003e \u003cp\u003e14.4.1 Base-Case Model 396\u003c\/p\u003e \u003cp\u003e14.4.2 Sensitivity Analysis 398\u003c\/p\u003e \u003cp\u003e14.4.3 Selecting Probability Distributions 399\u003c\/p\u003e \u003cp\u003e14.4.4 Simulation Analysis 399\u003c\/p\u003e \u003cp\u003e14.4.5 Simulation Sensitivity 402\u003c\/p\u003e \u003cp\u003e14.5 Option Pricing Using Simulation 404\u003c\/p\u003e \u003cp\u003e14.5.1 The Logic of Options 405\u003c\/p\u003e \u003cp\u003e14.5.2 Modeling Stock Prices 405\u003c\/p\u003e \u003cp\u003e14.5.3 Pricing an Option 408\u003c\/p\u003e \u003cp\u003e14.5.4 Sensitivity to Volatility 410\u003c\/p\u003e \u003cp\u003e14.5.5 Simulation Precision 410\u003c\/p\u003e \u003cp\u003e14.6 Selecting Uncertain Parameters 411\u003c\/p\u003e \u003cp\u003e14.7 Selecting Probability Distributions 413\u003c\/p\u003e \u003cp\u003e14.7.1 Empirical Data and Judgmental Data 413\u003c\/p\u003e \u003cp\u003e14.7.2 Six Essential Distributions 414\u003c\/p\u003e \u003cp\u003e14.7.3 Fitting Distributions to Data 418\u003c\/p\u003e \u003cp\u003e14.8 Ensuring Precision in Outputs 420\u003c\/p\u003e \u003cp\u003e14.8.1 Illustrations of Simulation Error 420\u003c\/p\u003e \u003cp\u003e14.8.2 Precision versus Accuracy 421\u003c\/p\u003e \u003cp\u003e14.8.3 An Experimental Method 422\u003c\/p\u003e \u003cp\u003e14.8.4 Precision Using the MSE 423\u003c\/p\u003e \u003cp\u003e14.8.5 Simulation Error in a Decision Context 423\u003c\/p\u003e \u003cp\u003e14.9 Interpreting Simulation Outcomes 424\u003c\/p\u003e \u003cp\u003e14.9.1 Simulation Results 424\u003c\/p\u003e \u003cp\u003e14.9.2 Displaying Results on the Spreadsheet 426\u003c\/p\u003e \u003cp\u003e14.10 When to Simulate and When Not To Simulate 426\u003c\/p\u003e \u003cp\u003e14.11 Summary 428\u003c\/p\u003e \u003cp\u003eSuggested Readings 428\u003c\/p\u003e \u003cp\u003eExercises 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 15 OPTIMIZATION IN SIMULATION 435\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 435\u003c\/p\u003e \u003cp\u003e15.2 Optimization with One or Two Decision Variables 435\u003c\/p\u003e \u003cp\u003e15.2.1 Base-case Model 436\u003c\/p\u003e \u003cp\u003e15.2.2 Grid Search 438\u003c\/p\u003e \u003cp\u003e15.2.3 Optimizing using Simulation Sensitivity 439\u003c\/p\u003e \u003cp\u003e15.2.4 Optimizing using Solver 442\u003c\/p\u003e \u003cp\u003e15.3 Stochastic Optimization 442\u003c\/p\u003e \u003cp\u003e15.3.1 Optimization of the Base-Case Model 442\u003c\/p\u003e \u003cp\u003e15.3.2 A Portfolio Optimization Problem 445\u003c\/p\u003e \u003cp\u003e15.4 Chance Constraints 448\u003c\/p\u003e \u003cp\u003e15.5 Two-Stage Problems with Recourse 453\u003c\/p\u003e \u003cp\u003e15.6 Summary 457\u003c\/p\u003e \u003cp\u003eSuggested Readings 458\u003c\/p\u003e \u003cp\u003eExercises 458\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMODELING CASES 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDIX 1 BASIC EXCEL SKILLS 479\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 479\u003c\/p\u003e \u003cp\u003eExcel Prerequisites 479\u003c\/p\u003e \u003cp\u003eThe Excel Window 480\u003c\/p\u003e \u003cp\u003eConfiguring Excel 482\u003c\/p\u003e \u003cp\u003eManipulating Windows and Sheets 483\u003c\/p\u003e \u003cp\u003eNavigation 484\u003c\/p\u003e \u003cp\u003eSelecting Cells 485\u003c\/p\u003e \u003cp\u003eEntering Text and Data 485\u003c\/p\u003e \u003cp\u003eEditing Cells 486\u003c\/p\u003e \u003cp\u003eFormatting 487\u003c\/p\u003e \u003cp\u003eBasic Formulas 488\u003c\/p\u003e \u003cp\u003eBasic Functions 489\u003c\/p\u003e \u003cp\u003eCharting 493\u003c\/p\u003e \u003cp\u003ePrinting 495\u003c\/p\u003e \u003cp\u003eHelp Options 496\u003c\/p\u003e \u003cp\u003eKeyboard Shortcuts 497\u003c\/p\u003e \u003cp\u003eCell Comments 497\u003c\/p\u003e \u003cp\u003eNaming Cells and Ranges 499\u003c\/p\u003e \u003cp\u003eSome Advanced Tools 502\u003c\/p\u003e \u003cp\u003eR1C1 Reference Style 502\u003c\/p\u003e \u003cp\u003eMixed Addresses 503\u003c\/p\u003e \u003cp\u003eAdvanced Functions 503\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDIX 2 MACROS AND VBA 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 507\u003c\/p\u003e \u003cp\u003eRecording a Macro 507\u003c\/p\u003e \u003cp\u003eEditing a Macro 510\u003c\/p\u003e \u003cp\u003eCreating a User-Defined Function 512\u003c\/p\u003e \u003cp\u003eSuggested Readings 514\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDIX 3 BASIC PROBABILITY CONCEPTS 515\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 515\u003c\/p\u003e \u003cp\u003eProbability Distributions 515\u003c\/p\u003e \u003cp\u003eExamples of Discrete Distributions 518\u003c\/p\u003e \u003cp\u003eExamples of Continuous Distributions 519\u003c\/p\u003e \u003cp\u003eExpected Values 521\u003c\/p\u003e \u003cp\u003eCumulative Distribution Functions 522\u003c\/p\u003e \u003cp\u003eTail Probabilities 523\u003c\/p\u003e \u003cp\u003eVariability 524\u003c\/p\u003e \u003cp\u003eSampling 525\u003c\/p\u003e \u003cp\u003eINDEX 529\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSteve Powell\u003c\/b\u003e is a Professor at the Tuck School of Business at Dartmouth College. His primary research interest lies in modeling production and service processes, but he has also been active in research in energy economics, marketing, and operations. At Tuck, he has developed a variety of courses in management science, including the core Decision Science course and electives in the Art of Modeling, Business Analytics, and Simulation. He originated the Teacher's Forum column in Interfaces, and he has written a number of articles on teaching modeling to practitioners. He was the Academic Director of the annual INFORMS Teaching of Management Science Workshops. In 2001, he was awarded the INFORMS Prize for the Teaching of Operations Research\/Management Science Practice. Along with Ken Baker, he has directed the Spreadsheet Engineering Research Project. In 2008, he co-authored \u003ci\u003eModeling for Insight: A Master Class for Business Analysts\u003c\/i\u003e with Robert J. Batt.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKen Baker\u003c\/b\u003e is a faculty member at Dartmouth College. He is currently the Nathaniel Leverone Professor of Management at the Tuck School of Business and Adjunct Professor at the Thayer School of Engineering. At Dartmouth, he has taught courses related to Management Science, Decision Support Systems, Manufacturing Management, and Environmental Management. Along with Steve Powell, he has directed the Spreadsheet Engineering Research Project. He is the author of two other textbooks, \u003ci\u003eOptimization Modeling with Spreadsheets\u003c\/i\u003e and \u003ci\u003ePrinciples of Sequencing and Scheduling\u003c\/i\u003e (with Dan Trietsch), in addition to a variety of technical articles. He has served as the Tuck School's Associate Dean and as the Co-Director of the Master's Program in Engineering Management.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988870152421,"sku":"NP9781119298427","price":107.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119298427.jpg?v=1761781851","url":"https:\/\/k12savings.com\/products\/business-analytics-isbn-9781119298427","provider":"K12savings","version":"1.0","type":"link"}