Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics 9th Edition, ISBN-13: 978-0357132098

Description

Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics 9th Edition, ISBN-13: 978-0357132098

[PDF eBook eTextbook]

• Publisher: ‎ Cengage Learning; 9th edition (September 28, 2021)
• Language: ‎ English
• 864 pages
• ISBN-10: ‎ 0357132092
• ISBN-13: ‎ 978-0357132098

3Master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 9E, written by respected business analytics innovator Cliff Ragsdale. This edition’s clear presentation, realistic examples, fascinating topics and valuable software provide everything you need to become proficient in today’s most widely used business analytics techniques using the latest version of Excel in Microsoft Office 365 or Office 2019. Become skilled in the newest Excel functions as well as Analytic Solver and Data Mining add-ins. This edition helps you develop both algebraic and spreadsheet modeling skills. Step-by-step instructions and annotated, full-color screen images make examples easy to follow and show you how to apply what you learn about descriptive, predictive and prescriptive analytics to real business situations.

Table of Contents:

Preface

Brief Contents

Contents

Chapter 1: Introduction to Modeling and Decision Analysis

1-0 Introduction

1-1 The Modeling Approach to Decision Making

1-2 Characteristics and Benefits of Modeling

1-3 Mathematical Models

1-4 Categories of Mathematical Models

1-5 Business Analytics and the Problem-Solving Process

1-6 Anchoring and Framing Effects

1-7 Good Decisions vs. Good Outcomes

1-8 Summary

1-9 References

Questions and Problems

Case 1-1 Patrick’s Paradox

Chapter 2: Introduction to Optimization and Linear Programming

2-0 Introduction

2-1 Applications of Mathematical Optimization

2-2 Characteristics of Optimization Problems

2-3 Expressing Optimization Problems Mathematically

2-4 Mathematical Programming Techniques

2-5 An Example LP Problem

2-6 Formulating LP Models

2-7 Summary of the LP Model for the Example Problem

2-8 The General Form of an LP Model

2-9 Solving LP Problems: An Intuitive Approach

2-10 Solving LP Problems: A Graphical Approach

2-11 Special Conditions in LP Models

2-12 Summary

2-13 References

Questions and Problems

Case 2-1 For the Lines They Are A-Changin’ (with Apologies to Bob Dylan)

Chapter 3: Modeling and Solving LP Problems in a Spreadsheet

3-0 Introduction

3-1 Spreadsheet Solvers

3-2 Solving LP Problems in a Spreadsheet

3-3 The Steps in Implementing an LP Model in a Spreadsheet

3-4 A Spreadsheet Model for the Blue Ridge Hot Tubs Problem

3-5 How Solver Views the Model

3-6 Using Analytic Solver

3-7 Using Excel’s Built-in Solver

3-8 Goals and Guidelines for Spreadsheet Design

3-9 Make vs. Buy Decisions

3-10 An Investment Problem

3-11 A Transportation Problem

3-12 A Blending Problem

3-13 A Production and Inventory Planning Problem

3-14 A Multiperiod Cash Flow Problem

3-15 Data Envelopment Analysis

3-16 Summary

3-17 References

Questions and Problems

Case 3-1 Putting the Link in the Supply Chain

Case 3-2 Foreign Exchange Trading at Baldwin Enterprises

Case 3-3 The Wolverine Retirement Fund

Case 3-4 Saving the Manatees

Chapter 4: Sensitivity Analysis and the Simplex Method

4-0 Introduction

4-1 The Purpose of Sensitivity Analysis

4-2 Approaches to Sensitivity Analysis

4-3 An Example Problem

4-4 The Answer Report

4-5 The Sensitivity Report

4-6 Ad Hoc Sensitivity Analysis

4-7 Robust Optimization

4-8 The Simplex Method

4-9 Summary

4-10 References

Questions and Problems

Case 4-1 A Nut Case

Case 4-2 Parket Sisters

Case 4-3 Kamm Industries

Chapter 5: Network Modeling

5-0 Introduction

5-1 The Transshipment Problem

5-2 The Shortest Path Problem

5-3 The Equipment Replacement Problem

5-4 Transportation/Assignment Problems

5-5 Generalized Network Flow Problems

5-6 Maximal Flow Problems

5-7 Special Modeling Considerations

5-8 Minimal Spanning Tree Problems

5-9 Summary

5-10 References

Questions and Problems

Case 5-1 Hamilton & Jovanovich

Case 5-2 Old Dominion Energy

Case 5-3 US Express

Case 5-4 The Major Electric Corporation

Chapter 6: Integer Linear Programming

6-0 Introduction

6-1 Integrality Conditions

6-2 Relaxation

6-3 Solving the Relaxed Problem

6-4 Bounds

6-5 Rounding

6-6 Stopping Rules

6-7 Solving ILP Problems Using Solver

6-8 Other ILP Problems

6-9 An Employee Scheduling Problem

6-10 Binary Variables

6-11 A Capital Budgeting Problem

6-12 Binary Variables and Logical Conditions

6-13 The Line Balancing Problem

6-14 The Fixed-Charge Problem

6-15 Minimum Order/Purchase Size

6-16 Quantity Discounts

6-17 A Contract Award Problem

6-18 The Branch-and-Bound Algorithm (Optional)

6-19 Summary

6-20 References

Questions and Problems

Case 6-1 Optimizing a Timber Harvest

Case 6-2 Power Dispatching at Old Dominion

Case 6-3 The MasterDebt Lockbox Problem

Case 6-4 Removing Snow in Montreal

Chapter 7: Goal Programming and Multiple Objective Optimization

7-0 Introduction

7-1 Goal Programming

7-2 A Goal Programming Example

7-3 Comments about Goal Programming

7-4 Multiple Objective Optimization

7-5 An MOLP Example

7-6 Comments on MOLP

7-7 Summary

7-8 References

Questions and Problems

Case 7-1 Removing Snow in Montreal

Case 7-2 Planning Diets for the Food Stamp Program

Case 7-3 Sales Territory Planning at Caro-Life

Chapter 8: Nonlinear Programming and Evolutionary Optimization

8-0 Introduction

8-1 The Nature of NLP Problems

8-2 Solution Strategies for NLP Problems

8-3 Local vs. Global Optimal Solutions

8-4 Economic Order Quantity Models

8-5 Location Problems

8-6 Nonlinear Network Flow Problem

8-7 Project Selection Problems

8-8 Optimizing Existing Financial Spreadsheet Models

8-9 The Portfolio Selection Problem

8-10 Sensitivity Analysis

8-11 Solver Options for Solving NLPs

8-12 Evolutionary Algorithms

8-13 Forming Fair Teams

8-14 The Traveling Salesperson Problem

8-15 Summary

8-16 References

Questions and Problems

Case 8-1 Tour de Europe

Case 8-2 Electing the Next President

Case 8-3 Making Windows at Wella

Case 8-4 Newspaper Advertising Insert Scheduling

Chapter 9: Regression Analysis

9-0 Introduction

9-1 An Example

9-2 Regression Models

9-3 Simple Linear Regression Analysis

9-4 Defining “Best Fit”

9-5 Solving the Problem Using Solver

9-6 Solving the Problem Using the Regression Tool

9-7 Evaluating the Fit

9-8 The R2 Statistic

9-9 Making Predictions

9-10 Statistical Tests for Population Parameters

9-11 Introduction to Multiple Regression

9-12 A Multiple Regression Example

9-13 Selecting the Model

9-14 Making Predictions

9-15 Other Model Selection Issues

9-16 Binary Independent Variables

9-17 Statistical Tests for the Population Parameters

9-18 Polynomial Regression

9-19 Summary

9-20 References

Questions and Problems

Case 9-1 Diamonds Are Forever

Case 9-2 Fiasco in Florida

Case 9-3 The Georgia Public Service Commission

Chapter 10: Data Mining

10-0 Introduction

10-1 Data Mining Overview

10-2 Classification

10-3 Classification Data Partitioning

10-4 Discriminant Analysis

10-5 Logistic Regression

10-6 k-Nearest Neighbor

10-7 Classification Trees

10-8 Neural Networks

10-9 Naive Bayes

10-10 Comments on Classification

10-11 Prediction

10-12 Association Rules (Affinity Analysis)

10-13 Cluster Analysis

10-14 Time Series

10-15 Summary

10-16 References

Questions and Problems

Case 10-1 Detecting Management Fraud

Chapter 11: Time Series Forecasting

11-0 Introduction

11-1 Time Series Methods

11-2 Measuring Accuracy

11-3 Stationary Models

11-4 Moving Averages

11-5 Weighted Moving Averages

11-6 Exponential Smoothing

11-7 Seasonality

11-8 Stationary Data with Additive Seasonal Effects

11-9 Stationary Data with Multiplicative Seasonal Effects

11-10 Trend Models

11-11 Double Moving Average

11-12 Double Exponential Smoothing (Holt’s Method)

11-13 Holt-Winter’s Method for Additive Seasonal Effects

11-14 Holt-Winter’s Method for Multiplicative Seasonal Effects

11-15 Modeling Time Series Trends Using Regression

11-16 Linear Trend Model

11-17 Quadratic Trend Model

11-18 Modeling Seasonality with Regression Models

11-19 Adjusting Trend Predictions with Seasonal Indices

11-20 Seasonal Regression Models

11-21 Combining Forecasts

11-22 Summary

11-23 References

Questions and Problems

Case 11-1 PB Chemical Corporation

Case 11-2 Forecasting COLAs

Case 11-3 Strategic Planning at Fysco Foods

Chapter 12: Introduction to Simulation Using Analytic Solver

12-0 Introduction

12-1 Random Variables and Risk

12-2 Why Analyze Risk?

12-3 Methods of Risk Analysis

12-4 A Corporate Health Insurance Example

12-5 Spreadsheet Simulation Using Analytic Solver

12-6 Random Number Generators

12-7 Preparing the Model for Simulation

12-8 Running the Simulation

12-9 Data Analysis

12-10 The Uncertainty of Sampling

12-11 Interactive Simulation

12-12 The Benefits of Simulation

12-13 Additional Uses of Simulation

12-14 A Reservation Management Example

12-15 An Inventory Control Example

12-16 A Project Selection Example

12-17 A Portfolio Optimization Example

12-18 Summary

12-19 References

Questions and Problems

Case 12-1 Live Well, Die Broke

Case 12-2 Death and Taxes

Case 12-3 The Sound’s Alive Company

Case 12-4 The Foxridge Investment Group

Chapter 13: Queuing Theory

13-0 Introduction

13-1 The Purpose of Queuing Models

13-2 Queuing System Configurations

13-3 Characteristics of Queuing Systems

13-4 Kendall Notation

13-5 Queuing Models

13-6 The M/M/s Model

13-7 The M/M/s Model with Finite Queue Length

13-8 The M/M/s Model with Finite Population

13-9 The M/G/1 Model

13-10 The M/D/1 Model

13-11 Simulating Queues and the Steady-State Assumption

13-12 Summary

13-13 References

Questions and Problems

Case 13-1 May the (Police) Force Be with You

Case 13-2 Call Center Staffing at Vacations Inc.

Case 13-3 Bullseye Department Store

Chapter 14: Decision Analysis

14-0 Introduction

14-1 Good Decisions vs. Good Outcomes

14-2 Characteristics of Decision Problems

14-3 An Example

14-4 The Payoff Matrix

14-5 Decision Rules

14-6 Nonprobabilistic Methods

14-7 Probabilistic Methods

14-8 The Expected Value of Perfect Information

14-9 Decision Trees

14-10 Creating Decision Trees with Analytic Solver

14-11 Multistage Decision Problems

14-12 Sensitivity Analysis

14-13 Using Sample Information in Decision Making

14-14 Computing Conditional Probabilities

14-15 Utility Theory

14-16 Multicriteria Decision Making

14-17 The Multicriteria Scoring Model

14-18 The Analytic Hierarchy Process

14-19 Summary

14-20 References

Questions and Problems

Case 14-1 Prezcott Pharma

Case 14-2 Hang on or Give Up?

Case 14-3 Should Larry Junior Go to Court or Settle?

Case 14-4 The Spreadsheet Wars

Chapter 15: Project Management

15-0 Introduction

15-1 An Example

15-2 Creating the Project Network

15-3 CPM: An Overview

15-4 The Forward Pass

15-5 The Backward Pass

15-6 Determining the Critical Path

15-7 Project Management Using Spreadsheets

15-8 Gantt Charts

15-9 Project Crashing

15-10 Pert: An Overview

15-11 Simulating Project Networks

15-12 Microsoft Project

15-13 Summary

15-14 References

Questions and Problems

Case 15-1 Project Management at a Crossroad

Case 15-2 The World Trade Center Clean-Up

Case 15-3 The Imagination Toy Corporation

Index

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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