Glossary
by Trevor S. Hale, Michael E. Hanna, Ralph M. Stair, Barry Render
Quantitative Analysis for Management, 13/e
- Quantitative Analysis for Management
- About the Authors
- Brief Contents
- Contents
- Preface
- Chapter 1 Introduction to Quantitative Analysis
- Learning Objectives
- 1.1 What Is Quantitative Analysis?
- 1.2 Business Analytics
- 1.3 The Quantitative Analysis Approach
- 1.4 How to Develop a Quantitative Analysis Model
- 1.5 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
- 1.6 Possible Problems in the Quantitative Analysis Approach
- 1.7 Implementation—Not Just the Final StepImplementation—Not Just the Final Step
- Summary
- Glossary
- Key Equations
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Chapter 2 Probability Concepts and Applications
- Learning Objectives
- 2.1 Fundamental Concepts
- 2.2 Revising Probabilities with Bayes’ TheoremRevising Probabilities with Bayes’ Theorem
- 2.3 Further Probability Revisions
- 2.4 Random Variables
- 2.5 Probability Distributions
- 2.6 The Binomial Distribution
- 2.7 The Normal Distribution
- 2.8 The F Distribution
- 2.9 The Exponential Distribution
- 2.10 The Poisson Distribution
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix 2.1: Derivation of Bayes’ TheoremDerivation of Bayes’ Theorem
- Chapter 3 Decision Analysis
- Learning Objectives
- 3.1 The Six Steps in Decision Making
- 3.2 Types of Decision-Making Environments
- 3.3 Decision Making Under Uncertainty
- 3.4 Decision Making Under Risk
- 3.5 Using Software for Payoff Table Problems
- 3.6 Decision Trees
- 3.7 How Probability Values Are Estimated by Bayesian Analysis
- 3.8 Utility Theory
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Chapter 4 Regression Models
- Learning Objectives
- 4.1 Scatter Diagrams
- 4.2 Simple Linear Regression
- 4.3 Measuring the Fit of the Regression Model
- 4.4 Assumptions of the Regression Model
- 4.5 Testing the Model for Significance
- 4.6 Using Computer Software for Regression
- 4.7 Multiple Regression Analysis
- 4.8 Binary or Dummy Variables
- 4.9 Model Building
- 4.10 Nonlinear Regression
- 4.11 Cautions and Pitfalls in Regression Analysis
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix 4.1: Formulas for Regression Calculations
- Chapter 5 Forecasting
- Learning Objectives
- 5.1 Types of Forecasting Models
- 5.2 Components of a Time-Series
- 5.3 Measures of Forecast Accuracy
- 5.4 Forecasting Models—Random Variations OnlyForecasting Models—Random Variations Only
- 5.5 Forecasting Models—Trend and Random VariationsForecasting Models—Trend and Random Variations
- 5.6 Adjusting for Seasonal Variations
- 5.7 Forecasting Models—Trend, Seasonal, and Random VariationsForecasting Models—Trend, Seasonal, and Random Variations
- 5.8 Monitoring and Controlling Forecasts
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Chapter 6 Inventory Control Models
- Learning Objectives
- 6.1 Importance of Inventory Control
- 6.2 Inventory Decisions
- 6.3 Economic Order Quantity: Determining How Much to Order
- 6.4 Reorder Point: Determining When to Order
- 6.5 EOQ Without the Instantaneous Receipt Assumption
- 6.6 Quantity Discount Models
- 6.7 Use of Safety Stock
- 6.8 Single-Period Inventory Models
- 6.9 ABC Analysis
- 6.10 Dependent Demand: The Case for Material Requirements Planning
- 6.11 Just-In-Time Inventory Control
- 6.12 Enterprise Resource Planning
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Chapter 7 Linear Programming Models: Graphical and Computer Methods
- Learning Objectives
- 7.1 Requirements of a Linear Programming Problem
- 7.2 Formulating LP Problems
- 7.3 Graphical Solution to an LP Problem
- Corner Point Solution Method
- Slack and Surplus
- 7.4 Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QMSolving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QM
- 7.5 Solving Minimization Problems
- 7.6 Four Special Cases in LP
- 7.7 Sensitivity Analysis
- High Note Sound Company
- Changes in the Objective Function Coefficient
- QM for Windows and Changes in Objective Function Coefficients
- Excel Solver and Changes in Objective Function Coefficients
- Changes in the Technological Coefficients
- Changes in the Resources or Right-Hand-Side Values
- QM for Windows and Changes in Right-Hand-Side Values
- Excel Solver and Changes in Right-Hand-Side Values
- Summary
- Glossary
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Chapter 8 Linear Programming Applications
- Chapter 9 Transportation, Assignment, and Network Models
- Chapter10Integer Programming, Goal Programming, and Nonlinear Programming
- Chapter 11 Project Management
- Learning Objectives
- 11.1 PERT/CPM
- 11.2 PERT/Cost
- 11.3 Project Crashing
- 11.4 Other Topics in Project Management
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix 11.1: Project Management with QM for Windows
- Chapter 12 Waiting Lines and Queuing Theory Models
- Learning Objectives
- 12.1 Waiting Line Costs
- 12.2 Characteristics of a Queuing System
- 12.3 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M /1)
- 12.4 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M ∕ ∕M ∕ ∕m)
- 12.5 Constant Service Time Model (M /D / 1)
- 12.6 Finite Population Model (M / M / 1 with Finite Source)
- 12.7 Some General Operating Characteristic Relationships
- 12.8 More Complex Queuing Models and the Use of Simulation
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix 12.1: Using QM for Windows
- Chapter 13 Simulation Modeling
- Learning Objectives
- 13.1 Advantages and Disadvantages of Simulation
- 13.2 Monte Carlo Simulation
- 13.3 Simulation and Inventory Analysis
- 13.4 Simulation of a Queuing Problem
- 13.5 Simulation Model for a Maintenance Policy
- Three Hills Power Company
- Column 1: Breakdown Number
- Column 2: Random Number for Breakdowns
- Column 3: Time Between Breakdowns
- Column 4: Time of Breakdown
- Column 5: Time Repairperson Is Free to Begin Repair
- Column 6: Random Number for Repair Time
- Column 7: Repair Time Required
- Column 8: Time Repair Ends
- Column 9: Number of Hours the Machine Is Down
- Cost Analysis of the Simulation
- Three Hills Power Company
- 13.6 Other Simulation Issues
- Summary
- Glossary
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Chapter 14 Markov Analysis
- Learning Objectives
- 14.1 States and State Probabilities
- 14.2 Matrix of Transition Probabilities
- 14.3 Predicting Future Market Shares
- 14.4 Markov Analysis of Machine Operations
- 14.5 Equilibrium Conditions
- 14.6 Absorbing States and the Fundamental Matrix: Accounts Receivable Application
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix 14.1: Markov Analysis with QM for Windows
- Appendix 14.2: Markov Analysis with Excel
- Chapter 15 Statistical Quality Control
- Appendices
- Appendix B: Binomial Probabilities
- Appendix C: Values of e - λ for Use in the Poisson Distribution
- Appendix D: F Distribution Values
- Appendix E: Using POM-QM for Windows
- Appendix F: Using Excel QM and Excel Add-Ins
- Appendix G: Solutions to Selected Problems
- Appendix H: Solutions to Self-Tests
- Index
- Module 1 Analytic Hierarchy Process
- Module 2 Dynamic Programming
- Module 3 Decision Theory and the Normal Distribution
- Learning Objectives
- M3.1 Break-Even Analysis and the Normal Distribution
- M3.2 Expected Value of Perfect Information and the Normal Distribution
- Summary
- Glossary
- Key Equations
- Solved Problems
- Self-Test
- Discussion Questions and Problems
- Bibliography
- Appendix M3.1: Derivation of the Break-Even Point
- Appendix M3.2: Unit Normal Loss Integral
- Module 4 Game Theory
- Module 5Mathematical Tools: Determinants and Matrices
- Module 6 Calculus-Based Optimization
- Module 7 Linear Programming: The Simplex Method
- Module 8 Transportation, Assignment, and Network Algorithms
Glossary
- 0–1 Integer Programming
Problems in which all decision variables must have integer values of 0 or 1. These variables are also called binary variables.
- Deviational Variables
Terms that are minimized in a goal programming problem. Like slack variables in LP, they are real. They are the only terms in the objective function.
- Global Optimum
The overall best solution to a nonlinear programming problem.
- Goal Programming
A mathematical programming technique that permits decision makers to set and prioritize multiple objectives.
- Integer Programming
A mathematical programming technique that produces integer solutions to linear programming problems.
- Local Optimum
A solution to a nonlinear programming problem that is better than any nearby point, but that may not be the global optimum.
- Nonlinear Programming
A category of mathematical programming techniques that allows the objective function and/or constraints to be nonlinear.
- Satisficing
The process of coming as close as possible to reaching your set of objectives.
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