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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
Overview
Special Features
Significant Changes to the Thirteenth Edition
Online Modules
Software
Companion Website
Instructor Resources
Chapter 1 Introduction to Quantitative Analysis
Learning Objectives
1.1 What Is Quantitative Analysis?
1.2 Business Analytics
1.3 The Quantitative Analysis Approach
Defining the Problem
Developing a Model
Acquiring Input Data
Developing a Solution
Testing the Solution
Analyzing the Results and Sensitivity Analysis
Implementing the Results
The Quantitative Analysis Approach and Modeling in the Real World
1.4 How to Develop a Quantitative Analysis Model
The Advantages of Mathematical Modeling
Mathematical Models Categorized by Risk
1.5 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
1.6 Possible Problems in the Quantitative Analysis Approach
Defining the Problem
Conflicting Viewpoints
Impact on Other Departments
Beginning Assumptions
Solution Outdated
Developing a Model
Fitting the Textbook Models
Understanding the Model
Acquiring Input Data
Using Accounting Data
Validity of Data
Developing a Solution
Hard-to-Understand Mathematics
Only One Answer is Limiting
Testing the Solution
Analyzing the Results
1.7 Implementation—Not Just the Final StepImplementation—Not Just the Final Step
Lack of Commitment and Resistance to Change
Lack of Commitment by Quantitative Analysts
Summary
Glossary
Key Equations
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Chapter 2 Probability Concepts and Applications
Learning Objectives
2.1 Fundamental Concepts
Two Basic Rules of Probability
Types of Probability
Mutually Exclusive and Collectively Exhaustive Events
Unions and Intersections of Events
Probability Rules for Unions, Intersections, and Conditional Probabilities
2.2 Revising Probabilities with Bayes’ TheoremRevising Probabilities with Bayes’ Theorem
General Form of Bayes’ Theorem
2.3 Further Probability Revisions
2.4 Random Variables
2.5 Probability Distributions
Probability Distribution of a Discrete Random Variable
Expected Value of a Discrete Probability Distribution
Variance of a Discrete Probability Distribution
Probability Distribution of a Continuous Random Variable
2.6 The Binomial Distribution
Solving Problems with the Binomial Formula
Solving Problems with Binomial Tables
2.7 The Normal Distribution
Area Under the Normal Curve
Using the Standard Normal Table
Haynes Construction Company Example
The Empirical Rule
2.8 The F Distribution
2.9 The Exponential Distribution
Arnold’s Muffler Example
2.10 The Poisson Distribution
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Solution
Solution
Solution
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
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
Optimistic
Pessimistic
Criterion of Realism (Hurwicz Criterion)
Equally Likely (Laplace)
Minimax Regret
3.4 Decision Making Under Risk
Expected Monetary Value
Expected Value of Perfect Information
Expected Opportunity Loss
Sensitivity Analysis
A Minimization Example
3.5 Using Software for Payoff Table Problems
QM for Windows
Excel QM
3.6 Decision Trees
Efficiency of Sample Information
Sensitivity Analysis
3.7 How Probability Values Are Estimated by Bayesian Analysis
Calculating Revised Probabilities
Potential Problem in Using Survey Results
3.8 Utility Theory
Measuring Utility and Constructing a Utility Curve
Utility as a Decision-Making Criterion
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
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
Coefficient of Determination
Correlation Coefficient
4.4 Assumptions of the Regression Model
Estimating the Variance
4.5 Testing the Model for Significance
Triple A Construction Example
The Analysis of Variance (ANOVA) Table
Triple A Construction ANOVA Example
4.6 Using Computer Software for Regression
Excel 2016
Excel QM
QM for Windows
4.7 Multiple Regression Analysis
Evaluating the Multiple Regression Model
Jenny Wilson Realty Example
4.8 Binary or Dummy Variables
4.9 Model Building
Stepwise Regression
Multicollinearity
4.10 Nonlinear Regression
4.11 Cautions and Pitfalls in Regression Analysis
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Appendix 4.1: Formulas for Regression Calculations
Chapter 5 Forecasting
Learning Objectives
5.1 Types of Forecasting Models
Qualitative Models
Causal Models
Time-Series 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
Moving Averages
Wallace Garden Supply Example
Weighted Moving Averages
Exponential Smoothing
Getting Started
Port of Baltimore Example
Using Software for Forecasting Time Series
5.5 Forecasting Models—Trend and Random VariationsForecasting Models—Trend and Random Variations
Exponential Smoothing with Trend
Trend Projections
5.6 Adjusting for Seasonal Variations
Seasonal Indices
Calculating Seasonal Indices with No Trend
Calculating Seasonal Indices with Trend
5.7 Forecasting Models—Trend, Seasonal, and Random VariationsForecasting Models—Trend, Seasonal, and Random Variations
The Decomposition Method
Software for Decomposition
Using Regression with Trend and Seasonal Components
5.8 Monitoring and Controlling Forecasts
Adaptive Smoothing
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Chapter 6 Inventory Control Models
Learning Objectives
6.1 Importance of Inventory Control
Decoupling Function
Storing Resources
Irregular Supply and Demand
Quantity Discounts
Avoiding Stockouts and Shortages
6.2 Inventory Decisions
6.3 Economic Order Quantity: Determining How Much to Order
Inventory Costs in the EOQ Situation
Finding the EOQ
Sumco Pump Company Example
Using Excel QM for Basic EOQ Inventory Problems
Purchase Cost of Inventory Items
Sensitivity Analysis with the EOQ Model
6.4 Reorder Point: Determining When to Order
6.5 EOQ Without the Instantaneous Receipt Assumption
Annual Carrying Cost for Production Run Model
Annual Setup Cost or Annual Ordering Cost
Determining the Optimal Production Quantity
Brown Manufacturing Example
Using Excel QM for Production Run Models
6.6 Quantity Discount Models
Brass Department Store Example
Using Excel QM for Quantity Discount Problems
6.7 Use of Safety Stock
6.8 Single-Period Inventory Models
Marginal Analysis with Discrete Distributions
Café du Donut Example
Marginal Analysis with the Normal Distribution
Newspaper Example
6.9 ABC Analysis
6.10 Dependent Demand: The Case for Material Requirements Planning
Material Structure Tree
Gross and Net Material Requirements Plans
Two or More End Products
6.11 Just-In-Time Inventory Control
6.12 Enterprise Resource Planning
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Appendix 6.1: Inventory Control with QM for Windows
Chapter 7 Linear Programming Models: Graphical and Computer Methods
Learning Objectives
7.1 Requirements of a Linear Programming Problem
7.2 Formulating LP Problems
Flair Furniture Company
7.3 Graphical Solution to an LP Problem
Graphical Representation of Constraints
Isoprofit Line Solution Method
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
Using QM for Windows
Using Excel’s Solver Command to Solve LP Problems
Preparing the Spreadsheet For Solver
Using Solver
Using Excel QM
7.5 Solving Minimization Problems
Holiday Meal Turkey Ranch
Using The Corner Point Method On A Minimization Problem
Isocost Line Approach
7.6 Four Special Cases in LP
No Feasible Solution
Unboundedness
Redundancy
Alternate Optimal Solutions
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
Solution
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Discussion Questions
Bibliography
Chapter 8 Linear Programming Applications
Learning Objectives
8.1 Marketing Applications
Media Selection
Marketing Research
8.2 Manufacturing Applications
Production Mix
Production Scheduling
8.3 Employee Scheduling Applications
Labor Planning
8.4 Financial Applications
Portfolio Selection
Truck Loading Problem
8.5 Ingredient Blending Applications
Diet Problems
Ingredient Mix and Blending Problems
8.6 Other Linear Programming Applications
Summary
Self-Test
Problems
Bibliography
Chapter 9 Transportation, Assignment, and Network Models
Learning Objectives
9.1 The Transportation Problem
Linear Program for the Transportation Example
Solving Transportation Problems Using Computer Software
A General LP Model for Transportation Problems
Facility Location Analysis
9.2 The Assignment Problem
Linear Program for Assignment Example
9.3 The Transshipment Problem
Linear Program for Transshipment Example
9.4 Maximal-Flow Problem
9.5 Shortest-Route Problem
9.6 Minimal-Spanning Tree Problem
Summary
Glossary
Solved Problems
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems*
Bibliography
Appendix 9.1: Using QM for Windows
Chapter10Integer Programming, Goal Programming, and Nonlinear Programming
Learning Objectives
10.1 Integer Programming
Harrison Electric Company Example of Integer Programming
Using Software to Solve the Harrison Integer Programming Problem
Mixed-Integer Programming Problem Example
Using QM For Windows And Excel to Solve Bagwell’s Integer Programming Model
10.2 Modeling with 0–1 (Binary) VariablesModeling with 0–1 (Binary) Variables
Capital Budgeting Example
Limiting the Number of Alternatives Selected
Dependent Selections
Fixed-Charge Problem Example
Financial Investment Example
10.3 Goal Programming
Extension to Equally Important Multiple Goals
Ranking Goals with Priority Levels
Goal Programming with Weighted Goals
Using QM For Windows To Solve Harrison’s Problem
10.4 Nonlinear Programming
Nonlinear Objective Function and Linear Constraints
Both Nonlinear Objective Function and Nonlinear Constraints
Linear Objective Function with Nonlinear Constraints
Summary
Glossary
Solved Problems
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Chapter 11 Project Management
Learning Objectives
11.1 PERT/CPM
General Foundry Example of PERT/CPM
Drawing the PERT/CPM Network
Activity Times
How to Find the Critical Path
Earliest Times
Latest Times
Concept Of Slack In Critical Path Computations
Probability of Project Completion
What PERT Was Able to Provide
Using Excel QM for the General Foundry Example
Sensitivity Analysis and Project Management
11.2 PERT/Cost
Planning and Scheduling Project Costs: Budgeting Process
Budgeting For General Foundry
Monitoring and Controlling Project Costs
11.3 Project Crashing
General Foundry Example
Project Crashing with Linear Programming
Objective Function
Crash Time Constraints
Project Completion Constraint
Constraints Describing The Network
11.4 Other Topics in Project Management
Subprojects
Milestones
Resource Leveling
Software
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Discussion Question
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
Three Rivers Shipping Company Example
12.2 Characteristics of a Queuing System
Arrival Characteristics
Size Of The Calling Population
Pattern Of Arrivals At The System
Behavior Of The Arrivals
Waiting Line Characteristics
Service Facility Characteristics
Basic Queuing System Configurations
Service Time Distribution
Identifying Models Using Kendall Notation
12.3 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M /1)
Assumptions of the Model
Queuing Equations
Arnold’s Muffler Shop Case
Using Excel QM On The Arnold’s Muffler Shop Queue
Introducing Costs Into The Model
Enhancing the Queuing Environment
12.4 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M ∕ ∕M ∕ ∕m)
Equations for the Multichannel Queuing Model
Arnold’s Muffler Shop Revisited
Using Excel QM For Analysis Of Arnold’s Multichannel Queuing Model
12.5 Constant Service Time Model (M /D / 1)
Equations for the Constant Service Time Model
Garcia-Golding Recycling, Inc.
Using Excel QM For Garcia-Golding’s Constant Service Time Model
12.6 Finite Population Model (M / M / 1 with Finite Source)
Equations for the Finite Population Model
Department of Commerce Example
Solving The Department Of Commerce Finite Population Model With Excel QM
12.7 Some General Operating Characteristic Relationships
12.8 More Complex Queuing Models and the Use of Simulation
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
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
Harry’s Auto Tire Example
Step 1: Establishing Probability Distributions.
Step 2: Building a Cumulative Probability Distribution for Each Variable.
Step 3: Setting Random Number Intervals.
Step 4: Generating Random Numbers.
Step 5: Simulating the Experiment.
Using QM for Windows for Simulation
Simulation with Excel Spreadsheets
13.3 Simulation and Inventory Analysis
Simkin’s Hardware Store
Analyzing Simkin’s Inventory Costs
13.4 Simulation of a Queuing Problem
Port of New Orleans
Using Excel to Simulate the Port of New Orleans 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
Building An Excel Simulation Model For Three Hills Power Company
13.6 Other Simulation Issues
Two Other Types of Simulation Models
Operational Gaming
Systems Simulation
Verification and Validation
Role of Computers in Simulation
Summary
Glossary
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Chapter 14 Markov Analysis
Learning Objectives
14.1 States and State Probabilities
The Vector of State Probabilities for Grocery Store Example
14.2 Matrix of Transition Probabilities
Transition Probabilities for Grocery Store Example
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
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Appendix 14.1: Markov Analysis with QM for Windows
Appendix 14.2: Markov Analysis with Excel
Chapter 15 Statistical Quality Control
Learning Objectives
15.1 Defining Quality and TQM
15.2 Statistical Process Control
Variability in the Process
Building Control Charts
Natural Variations
Assignable Variations
15.3 Control Charts for Variables
The Central Limit Theorem
Setting X -Chart Limits
Box-Filling Example
Using Excel QM For Box-Filling Example
Super Cola Example
Using Excel QM For Super Cola Example
Setting Range Chart Limits
Range Example
15.4 Control Charts for Attributes
p-Charts
ARCO p-Chart Example
Using Excel QM For p-Charts
c-Charts
Red Top Cab Company c-Chart Example
Using Excel QM For c-Charts
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Appendix 15.1: Using QM for Windows for SPC
Appendices
Appendix A: Areas Under the Standard Normal Curve
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
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z
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
Barclay Brothers Company’s New Product Decision
Probability Distribution of Demand
Using Expected Monetary Value to Make a Decision
M3.2 Expected Value of Perfect Information and the Normal Distribution
Opportunity Loss Function
Expected Opportunity Loss
Summary
Glossary
Key Equations
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
Appendix M3.1: Derivation of the Break-Even Point
Appendix M3.2: Unit Normal Loss Integral
Module 4 Game Theory
Learning Objectives
M4.1 Language of Games
M4.2 The Minimax Criterion
M4.3 Pure Strategy Games
M4.4 Mixed Strategy Games
M4.5 Dominance
Summary
Glossary
Solved Problems
Solution
Solution
Self-Test
Discussion Questions and Problems
Discussion Questions
Problems
Bibliography
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
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Chapter 9 Transportation, Assignment, and Network Models
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