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Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics 8th edition [Kõva köide]

3.25/5 (24 hinnangut Goodreads-ist)

Cliff Ragsdale (Virginia Polytechnic Institute and State University)

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Formaat: Hardback, 864 pages, kõrgus x laius x paksus: 38x210x256 mm, kaal: 1678 g
Ilmumisaeg: 01-Jan-2017
Kirjastus: South-Western College Publishing
ISBN-10: 130594741X
ISBN-13: 9781305947412

Teised raamatud teemal:

Management decision making - (Hetkel poes: 1 nimetust)
Economic statistics
Business mathematics & systems
Spreadsheet software

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Formaat: Hardback, 864 pages, kõrgus x laius x paksus: 38x210x256 mm, kaal: 1678 g
Ilmumisaeg: 01-Jan-2017
Kirjastus: South-Western College Publishing
ISBN-10: 130594741X
ISBN-13: 9781305947412

Teised raamatud teemal:

Management decision making - (Hetkel poes: 1 nimetust)
Economic statistics
Business mathematics & systems
Spreadsheet software

Püsilink: https://www.kriso.ee/db/9781305947412.html

Märksõnad:

Valuable software, realistic examples, clear writing, and fascinating topics help you master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS, 8E. Youll find everything you need to become proficient in todays most widely used business analytics techniques using Microsoft® Office Excel® 2016. Author Cliff Ragsdale -- respected innovator in business analytics -- guides you through the skills you need, using the latest Excel® for Windows. You gain the confidence to apply what you learn to real business situations with step-by-step instructions and annotated screen images that make examples easy to follow. The World of Management Science sections further demonstrates how each topic applies to a real company.

Each new edition includes extended trial licenses for Analytic Solver Platform and XLMiner with powerful simulation and optimization tools for descriptive and prescriptive analytics and a full suite of tools for data mining in Excel.

1 Introduction to Modeling and Decision Analysis

(16)

Introduction

(2)

The Modeling Approach to Decision Making

(1)

Characteristics and Benefits of Modeling

(1)

Mathematical Models

(2)

Categories of Mathematical Models

(1)

Business Analytics and the Problem-Solving Process

(3)

Anchoring and Framing Effects

(1)

Good Decisions vs. Good Outcomes

(1)

Summary

(1)

References

(2)

Questions and Problems

(1)

Case

(2)

2 Introduction to Optimization and Linear Programming

(29)

Introduction

(1)

Applications of Mathematical Optimization

(1)

Characteristics of Optimization Problems

(1)

Expressing Optimization Problems Mathematically

(1)

Decisions

(1)

Constraints

(1)

Objective

(1)

Mathematical Programming Techniques

(1)

An Example LP Problem

(1)

Formulating LP Models

(1)

Steps in Formulating an LP Model

(2)

Summary of the LP Model for the Example Problem

(1)

The General Form of an LP Model

(1)

Solving LP Problems: An Intuitive Approach

(1)

Solving LP Problems: A Graphical Approach

(1)

Plotting the First Constraint

(1)

Plotting the Second Constraint

(1)

Plotting the Third Constraint

(1)

The Feasible Region

(1)

Plotting the Objective Function

(1)

Finding the Optimal Solution Using Level Curves

(3)

Finding the Optimal Solution by Enumerating the Corner Points

(1)

Summary of Graphical Solution to LP Problems

(1)

Understanding How Things Change

(1)

Special Conditions in LP Models

(1)

Alternate Optimal Solutions

(1)

Redundant Constraints

(2)

Unbounded Solutions

(1)

Infeasibility

(1)

Summary

(1)

References

(1)

Questions and Problems

(5)

Case

(2)

3 Modeling and Solving LP Problems in a Spreadsheet

(95)

Introduction

(1)

Spreadsheet Solvers

(1)

Solving LP Problems in a Spreadsheet

(1)

The Steps in Implementing an LP Model in a Spreadsheet

(2)

A Spreadsheet Model for the Blue Ridge Hot Tubs Problem

(1)

Organizing the Data

(1)

Representing the Decision Variables

(1)

Representing the Objective Function

(1)

Representing the Constraints

(1)

Representing the Bounds on the Decision Variables

(1)

How Solver Views the Model

(2)

Using Analytic Solver Platform

(1)

Defining the Objective Cell

(1)

Defining the Variable Cells

(1)

Defining the Constraint Cells

(3)

Defining the Nonnegativity Conditions

(1)

Reviewing the Model

(1)

Other Options

(1)

Solving the Problem

(1)

Using Excel's Built-in Solver

(1)

Goals and Guidelines for Spreadsheet Design

(2)

Make vs. Buy Decisions

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(1)

An Investment Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Transportation Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Heuristic Solution for the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Blending Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Some Observations about Constraints, Reporting, and Scaling

(1)

Re-scaling the Model

(1)

Implementing the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(2)

A Production and Inventory Planning Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Multiperiod Cash Flow Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(2)

Implementing the Model

(2)

Solving the Problem

100

(1)

Analyzing the Solution

101

(1)

Modifying the Taco-Viva Problem to Account for Risk (Optional)

102

(2)

Implementing the Risk Constraints

104

(1)

Solving the Problem

105

(1)

Analyzing the Solution

105

(1)

Data Envelopment Analysis

106

(1)

Defining the Decision Variables

107

(1)

Defining the Objective

107

(1)

Defining the Constraints

107

(1)

Implementing the Model

108

(2)

Solving the Problem

110

(3)

Analyzing the Solution

113

(1)

Summary

114

(1)

References

115

(1)

Questions and Problems

116

(18)

Case

134

(7)

4 Sensitivity Analysis and the Simplex Method

141

(48)

Introduction

141

(1)

The Purpose of Sensitivity Analysis

141

(1)

Approaches to Sensitivity Analysis

142

(1)

An Example Problem

142

(1)

The Answer Report

143

(2)

The Sensitivity Report

145

(1)

Changes in the Objective Function Coefficients

145

(2)

A Comment about Constancy

147

(1)

Alternate Optimal Solutions

148

(1)

Changes in the RHS Values

148

(1)

Shadow Prices for Nonbinding Constraints

149

(1)

A Note about Shadow Prices

149

(2)

Shadow Prices and the Value of Additional Resources

151

(1)

Other Uses of Shadow Prices

151

(1)

The Meaning of the Reduced Costs

152

(2)

Analyzing Changes in Constraint Coefficients

154

(1)

Simultaneous Changes in Objective Function Coefficients

155

(1)

A Warning about Degeneracy

156

(1)

The Limits Report

156

(1)

Ad Hoc Sensitivity Analysis

157

(1)

Creating Spider Plots and Tables

157

(4)

Creating a Solver Table

161

(3)

Comments

164

(1)

Robust Optimization

164

(4)

The Simplex Method

168

(1)

Creating Equality Constraints Using Slack Variables

168

(1)

Basic Feasible Solutions

169

(3)

Finding the Best Solution

172

(1)

Summary

172

(1)

References

172

(1)

Questions and Problems

173

(10)

Case

183

(6)

5 Network Modeling

189

(58)

Introduction

189

(1)

The Transshipment Problem

189

(1)

Characteristics of Network Flow Problems

189

(2)

The Decision Variables for Network Flow Problems

191

(1)

The Objective Function for Network Flow Problems

191

(1)

The Constraints for Network Flow Problems

192

(1)

Implementing the Model in a Spreadsheet

193

(2)

Analyzing the Solution

195

(1)

The Shortest Path Problem

196

(2)

An LP Model for the Example Problem

198

(1)

The Spreadsheet Model and Solution

198

(2)

Network Flow Models and Integer Solutions

200

(1)

The Equipment Replacement Problem

201

(1)

The Spreadsheet Model and Solution

202

(2)

Transportation/Assignment Problems

204

(1)

Generalized Network Flow Problems

205

(2)

Formulating an LP Model for the Recycling Problem

207

(1)

Implementing the Model

208

(2)

Analyzing the Solution

210

(1)

Generalized Network Flow Problems and Feasibility

211

(3)

Maximal Flow Problems

214

(1)

An Example of a Maximal Flow Problem

214

(2)

The Spreadsheet Model and Solution

216

(2)

Special Modeling Considerations

218

(3)

Minimal Spanning Tree Problems

221

(1)

An Algorithm for the Minimal Spanning Tree Problem

222

(1)

Solving the Example Problem

222

(1)

Summary

223

(1)

References

223

(2)

Questions and Problems

225

(16)

Case

241

(6)

6 Integer Linear Programming

247

(79)

Introduction

247

(1)

Integrality Conditions

247

(1)

Relaxation

248

(1)

Solving the Relaxed Problem

249

(1)

Bounds

250

(2)

Rounding

252

(2)

Stopping Rules

254

(1)

Solving ILP Problems Using Solver

255

(3)

Other ILP Problems

258

(1)

An Employee Scheduling Problem

259

(1)

Defining the Decision Variables

260

(1)

Defining the Objective Function

260

(1)

Defining the Constraints

260

(1)

A Note about the Constraints

261

(1)

Implementing the Model

261

(2)

Solving the Model

263

(1)

Analyzing the Solution

263

(1)

Binary Variables

264

(1)

A Capital Budgeting Problem

264

(1)

Defining the Decision Variables

265

(1)

Defining the Objective Function

265

(1)

Defining the Constraints

265

(1)

Setting Up the Binary Variables

265

(1)

Implementing the Model

265

(1)

Solving the Model

266

(2)

Comparing the Optimal Solution to a Heuristic Solution

268

(1)

Binary Variables and Logical Conditions

268

(1)

The Line Balancing Problem

269

(1)

Defining the Decision Variables

269

(1)

Defining the Constraints

270

(1)

Defining the Objective

271

(1)

Implementing the Model

272

(2)

Analyzing the Solution

274

(1)

Extension

275

(3)

The Fixed-Charge Problem

278

(1)

Defining the Decision Variables

279

(1)

Defining the Objective Function

279

(1)

Defining the Constraints

280

(1)

Determining Values for "Big M"

280

(1)

Implementing the Model

281

(1)

Solving the Model

282

(1)

Analyzing the Solution

283

(1)

A Comment on IF() Functions

284

(1)

Minimum Order/Purchase Size

285

(1)

Quantity Discounts

286

(1)

Formulating the Model

286

(1)

The Missing Constraints

287

(1)

A Contract Award Problem

287

(1)

Formulating the Model: The Objective Function and Transportation Constraints

288

(1)

Implementing the Transportation Constraints

289

(1)

Formulating the Model: The Side Constraints

290

(1)

Implementing the Side Constraints

291

(1)

Solving the Model

292

(1)

Analyzing the Solution

293

(1)

The Branch-and-Bound Algorithm (Optional)

293

(2)

Branching

295

(1)

Bounding

296

(1)

Branching Again

296

(2)

Bounding Again

298

(1)

Summary of B&B Example

299

(1)

Summary

300

(1)

References

301

(1)

Questions and Problems

302

(19)

Case

321

(5)

7 Goal Programming and Multiple Objective Optimization

326

(45)

Introduction

326

(1)

Goal Programming

326

(1)

A Goal Programming Example

327

(1)

Defining the Decision Variables

328

(1)

Defining the Goals

328

(1)

Defining the Goal Constraints

328

(1)

Defining the Hard Constraints

329

(1)

GP Objective Functions

330

(1)

Defining the Objective

331

(1)

Implementing the Model

332

(1)

Solving the Model

333

(1)

Analyzing the Solution

334

(1)

Revising the Model

335

(1)

Trade-offs: The Nature of GP

336

(1)

Comments about Goal Programming

337

(1)

Multiple Objective Optimization

338

(2)

An MOLP Example

340

(1)

Defining the Decision Variables

340

(1)

Defining the Objectives

340

(1)

Defining the Constraints

341

(1)

Implementing the Model

341

(1)

Determining Target Values for the Objectives

342

(2)

Summarizing the Target Solutions

344

(1)

Determining a GP Objective

345

(2)

The Minimax Objective

347

(1)

Implementing the Revised Model

348

(1)

Solving the Model

348

(3)

Comments on MOLP

351

(1)

Summary

352

(1)

References

352

(1)

Questions and Problems

353

(13)

Case

366

(5)

8 Nonlinear Programming & Evolutionary Optimization

371

(76)

Introduction

371

(1)

The Nature of NLP Problems

371

(2)

Solution Strategies for NLP Problems

373

(1)

Local vs. Global Optimal Solutions

374

(2)

Economic Order Quantity Models

376

(3)

Implementing the Model

379

(1)

Solving the Model

379

(2)

Analyzing the Solution

381

(1)

Comments on the EOQ Model

381

(1)

Location Problems

382

(1)

Defining the Decision Variables

383

(1)

Defining the Objective

383

(1)

Defining the Constraints

384

(1)

Implementing the Model

384

(1)

Solving the Model and Analyzing the Solution

385

(1)

Another Solution to the Problem

386

(2)

Some Comments about the Solution to Location Problems

388

(1)

Nonlinear Network Flow Problem

388

(1)

Defining the Decision Variables

388

(1)

Defining the Objective

388

(1)

Defining the Constraints

389

(1)

Implementing the Model

390

(1)

Solving the Model and Analyzing the Solution

391

(2)

Project Selection Problems

393

(1)

Defining the Decision Variables

393

(1)

Defining the Objective Function

394

(1)

Defining the Constraints

394

(1)

Implementing the Model

395

(1)

Solving the Model

396

(2)

Optimizing Existing Financial Spreadsheet Models

398

(1)

Implementing the Model

398

(1)

Optimizing the Spreadsheet Model

399

(2)

Analyzing the Solution

401

(1)

Comments on Optimizing Existing Spreadsheets

401

(1)

The Portfolio Selection Problem

401

(2)

Defining the Decision Variables

403

(1)

Defining the Objective

403

(1)

Defining the Constraints

404

(1)

Implementing the Model

404

(2)

Analyzing the Solution

406

(2)

Handling Conflicting Objectives in Portfolio Problems

408

(2)

Sensitivity Analysis

410

(3)

Lagrange Multipliers

413

(1)

Reduced Gradients

413

(1)

Solver Options for Solving NLPs

413

(2)

Evolutionary Algorithms

415

(1)

Forming Fair Teams

416

(1)

A Spreadsheet Model for the Problem

417

(1)

Solving the Model

418

(1)

Analyzing the Solution

418

(1)

The Traveling Salesperson Problem

419

(1)

A Spreadsheet Model for the Problem

420

(2)

Solving the Model

422

(1)

Analyzing the Solution

423

(1)

Summary

424

(1)

References

424

(1)

Questions and Problems

425

(17)

Case

442

(5)

9 Regression Analysis

447

(52)

Introduction

447

(1)

An Example

447

(2)

Regression Models

449

(1)

Simple Linear Regression Analysis

450

(1)

Defining "Best Fit"

451

(1)

Solving the Problem Using Solver

452

(2)

Solving the Problem Using the Regression Tool

454

(2)

Evaluating the Fit

456

(2)

The R2 Statistic

458

(2)

Making Predictions

460

(1)

The Standard Error

460

(1)

Prediction Intervals for New Values of Y

461

(2)

Confidence Intervals for Mean Values of Y

463

(1)

Extrapolation

463

(1)

Statistical Tests for Population Parameters

464

(1)

Analysis of Variance

464

(1)

Assumptions for the Statistical Tests

465

(1)

Statistical Tests

466

(1)

Introduction to Multiple Regression

467

(2)

A Multiple Regression Example

469

(1)

Selecting the Model

470

(1)

Models with One Independent Variable

471

(1)

Models with Two Independent Variables

471

(3)

Inflating R2

474

(1)

The Adjusted-R2 Statistic

474

(1)

The Best Model with Two Independent Variables

475

(1)

Multicollinearity

475

(1)

The Model with Three Independent Variables

475

(2)

Making Predictions

477

(1)

Binary Independent Variables

478

(1)

Statistical Tests for the Population Parameters

478

(1)

Polynomial Regression

479

(1)

Expressing Nonlinear Relationships Using Linear Models

480

(4)

Summary of Nonlinear Regression

484

(1)

Summary

484

(1)

References

485

(1)

Questions and Problems

486

(8)

Case

494

(5)

10 Data Mining

499

(67)

Introduction

499

(1)

Data Mining Overview

499

(3)

Classification

502

(1)

A Classification Example

503

(7)

Classification Data Partitioning

510

(2)

Discriminant Analysis

512

(2)

Discriminant Analysis Example

514

(6)

Logistic Regression

520

(2)

Logistic Regression Example

522

(3)

k-Nearest Neighbor

525

(2)

k-Nearest Neighbor Example

527

(1)

Classification Trees

528

(4)

Classification Tree Example

532

(4)

Neural Networks

536

(1)

Neural Network Example

537

(2)

Naive Bayes

539

(4)

Naive Bayes Example

543

(3)

Comments on Classification

546

(1)

Combining Classifications

547

(1)

The Role of Test Data

547

(1)

Prediction

548

(1)

Association Rules (Affinity Analysis)

548

(2)

Association Rules Example

550

(1)

Cluster Analysis

551

(2)

Cluster Analysis Example

553

(1)

k-Mean Clustering Example

553

(3)

Hierarchical Clustering Example

556

(1)

Time Series

557

(1)

Summary

558

(1)

References

559

(1)

Questions and Problems

560

(4)

Case

564

(2)

11 Time Series Forecasting

566

(69)

Introduction

566

(1)

Time Series Methods

567

(1)

Measuring Accuracy

567

(1)

Stationary Models

568

(1)

Moving Averages

569

(2)

Forecasting with the Moving Average Model

571

(1)

Weighted Moving Averages

572

(3)

Forecasting with the Weighted Moving Average Model

575

(1)

Exponential Smoothing

575

(1)

Forecasting with the Exponential Smoothing Model

576

(2)

Seasonality

578

(1)

Stationary Data with Additive Seasonal Effects

579

(4)

Forecasting with the Model

583

(1)

Stationary Data with Multiplicative Seasonal Effects

584

(2)

Forecasting with the Model

586

(1)

Trend Models

587

(1)

An Example

588

(1)

Double Moving Average

589

(1)

Forecasting with the Model

590

(1)

Double Exponential Smoothing (Holt's Method)

591

(3)

Forecasting with Holt's Method

594

(1)

Holt-Winter's Method for Additive Seasonal Effects

595

(4)

Forecasting with Holt-Winter's Additive Method

599

(1)

Holt-Winter's Method for Multiplicative Seasonal Effects

599

(4)

Forecasting with Holt-Winter's Multiplicative Method

603

(1)

Modeling Time Series Trends Using Regression

603

(1)

Linear Trend Model

603

(2)

Forecasting with the Linear Trend Model

605

(1)

Quadratic Trend Model

606

(2)

Forecasting with the Quadratic Trend Model

608

(1)

Modeling Seasonality with Regression Models

609

(1)

Adjusting Trend Predictions with Seasonal Indices

609

(1)

Computing Seasonal Indices

609

(2)

Forecasting with Seasonal Indices

611

(2)

Refining the Seasonal Indices

613

(2)

Seasonal Regression Models

615

(1)

The Seasonal Model

615

(3)

Forecasting with the Seasonal Regression Model

618

(1)

Combining Forecasts

619

(1)

Summary

619

(1)

References

620

(1)

Questions and Problems

621

(9)

Case

630

(5)

12 Introduction to Simulation Using Analytic Solver Platform

635

(86)

Introduction

635

(1)

Random Variables and Risk

635

(1)

Why Analyze Risk?

636

(1)

Methods of Risk Analysis

636

(1)

Best-Case/Worst-Case Analysis

637

(1)

What-If Analysis

638

(1)

Simulation

638

(1)

A Corporate Health Insurance Example

639

(2)

A Critique of the Base Case Model

641

(1)

Spreadsheet Simulation Using Analytic Solver Platform

641

(1)

Starting Analytic Solver Platform

642

(1)

Random Number Generators

642

(2)

Discrete vs. Continuous Random Variables

644

(1)

Preparing the Model for Simulation

645

(2)

Alternate RNG Entry

647

(2)

Running the Simulation

649

(1)

Selecting the Output Cells to Track

649

(1)

Selecting the Number of Replications

650

(1)

Selecting What Gets Displayed on the Worksheet

651

(1)

Running the Simulation

652

(1)

Data Analysis

652

(1)

The Best Case and the Worst Case

652

(1)

The Frequency Distribution of the Output Cells

653

(1)

The Cumulative Distribution of the Output Cells

654

(1)

Obtaining Other Cumulative Probabilities

655

(1)

Sensitivity Analysis

656

(1)

The Uncertainty of Sampling

657

(1)

Constructing a Confidence Interval for the True Population Mean

657

(2)

Constructing a Confidence Interval for a Population Proportion

659

(1)

Sample Sizes and Confidence Interval Widths

659

(1)

Interactive Simulation

660

(1)

The Benefits of Simulation

661

(1)

Additional Uses of Simulation

662

(1)

A Reservation Management Example

662

(1)

Implementing the Model

663

(1)

Details for Multiple Simulations

664

(2)

Running the Simulations

666

(1)

Data Analysis

666

(1)

An Inventory Control Example

667

(2)

Creating the RNGs

669

(1)

Implementing the Model

670

(3)

Replicating the Model

673

(1)

Optimizing the Model

674

(6)

Analyzing the Solution

680

(2)

Other Measures of Risk

682

(2)

A Project Selection Example

684

(1)

A Spreadsheet Model

684

(2)

Solving and Analyzing the Problem with Analytic Solver Platform

686

(1)

Considering Another Solution

687

(2)

A Portfolio Optimization Example

689

(1)

A Spreadsheet Model

690

(2)

Solving the Problem with Analytic Solver Platform

692

(3)

Summary

695

(1)

References

695

(1)

Questions and Problems

696

(15)

Case

711

(10)

13 Queuing Theory

721

(33)

Introduction

721

(1)

The Purpose of Queuing Models

721

(1)

Queuing System Configurations

722

(1)

Characteristics of Queuing Systems

723

(1)

Arrival Rate

724

(1)

Service Rate

725

(2)

Kendall Notation

727

(1)

Queuing Models

727

(2)

The M/M/s Model

729

(1)

An Example

730

(1)

The Current Situation

730

(1)

Adding a Server

731

(1)

Economic Analysis

732

(1)

The M/M/s Model with Finite Queue Length

732

(1)

The Current Situation

733

(1)

Adding a Server

734

(1)

The M/M/s Model with Finite Population

734

(1)

An Example

735

(1)

The Current Situation

736

(1)

Adding Servers

737

(1)

The M/G/1 Model

738

(1)

The Current Situation

739

(1)

Adding the Automated Dispensing Device

740

(2)

The M/D/1 Model

742

(1)

Simulating Queues and the Steady-State Assumption

742

(1)

Summary

743

(1)

References

743

(2)

Questions and Problems

745

(6)

Case

751

(3)

14 Decision Analysis

754

(75)

Introduction

754

(1)

Good Decisions vs. Good Outcomes

754

(1)

Characteristics of Decision Problems

755

(1)

An Example

755

(1)

The Payoff Matrix

756

(1)

Decision Alternatives

756

(1)

States of Nature

757

(1)

The Payoff Values

757

(1)

Decision Rules

758

(1)

Nonprobabilistic Methods

758

(1)

The Maximax Decision Rule

759

(1)

The Maximin Decision Rule

760

(1)

The Minimax Regret Decision Rule

760

(2)

Probabilistic Methods

762

(1)

Expected Monetary Value

763

(1)

Expected Regret

764

(1)

Sensitivity Analysis

765

(2)

The Expected Value of Perfect Information

767

(2)

Decision Trees

769

(1)

Rolling Back a Decision Tree

770

(1)

Creating Decision Trees with Analytic Solver Platform

771

(1)

Adding Event Nodes

772

(3)

Determining the Payoffs and EMVs

775

(1)

Other Features

776

(1)

Multistage Decision Problems

777

(1)

A Multistage Decision Tree

778

(1)

Developing a Risk Profile

779

(1)

Sensitivity Analysis

780

(1)

Tornado Charts

781

(3)

Strategy Tables

784

(2)

Strategy Charts

786

(2)

Using Sample Information in Decision Making

788

(1)

Conditional Probabilities

789

(1)

The Expected Value of Sample Information

790

(1)

Computing Conditional Probabilities

791

(2)

Bayes's Theorem

793

(1)

Utility Theory

794

(1)

Utility Functions

794

(1)

Constructing Utility Functions

795

(3)

Using Utilities to Make Decisions

798

(1)

The Exponential Utility Function

798

(1)

Incorporating Utilities in Decision Trees

799

(1)

Multicriteria Decision Making

800

(1)

The Multicriteria Scoring Model

801

(4)

The Analytic Hierarchy Process

805

(1)

Pairwise Comparisons

805

(1)

Normalizing the Comparisons

806

(1)

Consistency

807

(3)

Obtaining Scores for the Remaining Criteria

810

(1)

Obtaining Criterion Weights

810

(1)

Implementing the Scoring Model

810

(1)

Summary

811

(1)

References

811

(2)

Questions and Problems

813

(10)

Case

823

(6)

Index

829

A leading innovator in spreadsheet instruction and highly regarded pioneer in business analytics, Dr. Cliff Ragsdale is the Bank of America Professor of Business Information Technology and Academic Director of the Center for Business Intelligence and Analytics in the Pamplin College of Business at Virginia Tech, where he has taught since 1990. Dr. Ragsdale received his Ph.D. in management science and information technology from the University of Georgia. He also holds an M.B.A. in Finance and B.A. in psychology from the University of Central Florida. Before pursuing his Ph.D., he supervised benefit finance and qualified plans at the international headquarters of Red Lobster, Inc. He has served as an information systems and statistical consultant for a variety of companies and as an expert witness in the area of spreadsheet forensics. Dr. Ragsdale's primary areas of research interest include applications of artificial intelligence, mathematical programming and applying statistics to business problems. His research has appeared in numerous publications, including Decision Sciences; Naval Research Logistics; Omega: The International Journal of Management Science; Computers & Operations Research; Operations Research Letters and Personal Financial Planning. He has received both the Pamplin Award for excellence in teaching and the Outstanding Doctoral Educator Award from the Pamplin College of Business Administration at Virginia Tech. Dr. Ragsdale is a fellow of the Decision Sciences Institute (DSI) and active member of DSI and INFORMS.

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