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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