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Testbank for Quantitative Analysis for Management: (12th Edition) by Barry Render

By: Barry Render , Ralph M. Stair , Michael E. Hanna , Trevor S. Hale
ISBN-10: 133507335
/ ISBN-13: 9780133507331

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Format: Downloadable ZIP Fille
Authors: Barry Render , Ralph M. Stair , Michael E. Hanna , Trevor S. Hale
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Table of content:

Title Page
Copyright Page
About the Authors
Contents
PREFACE
CHAPTER 1 Introduction to Quantitative Analysis
1.1 Introduction
1.2 What Is Quantitative Analysis?
1.3 Business Analytics
1.4 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
1.5 How to Develop a Quantitative Analysis Model
The Advantages of Mathematical Modeling
Mathematical Models Categorized by Risk
1.6 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
1.7 Possible Problems in the Quantitative Analysis Approach
Defining the Problem
Developing a Model
Acquiring Input Data
Developing a Solution
Testing the Solution
Analyzing the Results
1.8 Implementation—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
Case Study: Food and Beverages at Southwestern University Football Games
Bibliography
CHAPTER 2 Probability Concepts and Applications
2.1 Introduction
2.2 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
2.3 Revising Probabilities with Bayes’ Theorem
General Form of Bayes’ Theorem
2.4 Further Probability Revisions
2.5 Random Variables
2.6 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
2.7 The Binomial Distribution
Solving Problems with the Binomial Formula
Solving Problems with Binomial Tables
2.8 The Normal Distribution
Area Under the Normal Curve
Using the Standard Normal Table
Haynes Construction Company Example
The Empirical Rule
2.9 The F Distribution
2.10 The Exponential Distribution
Arnold’s Muffler Example
2.11 The Poisson Distribution
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: WTVX
Bibliography
Appendix 2.1: Derivation of Bayes’ Theorem
CHAPTER 3 Decision Analysis
3.1 Introduction
3.2 The Six Steps in Decision Making
3.3 Types of Decision-Making Environments
3.4 Decision Making Under Uncertainty
Optimistic
Pessimistic
Criterion of Realism (Hurwicz Criterion)
Equally Likely (Laplace)
Minimax Regret
3.5 Decision Making Under Risk
Expected Monetary Value
Expected Value of Perfect Information
Expected Opportunity Loss
Sensitivity Analysis
3.6 A Minimization Example
3.7 Using Software for Payoff Table Problems
QM for Windows
Excel QM
3.8 Decision Trees
Efficiency of Sample Information
Sensitivity Analysis
3.9 How Probability Values Are Estimated by Bayesian Analysis
Calculating Revised Probabilities
Potential Problem in Using Survey Results
3.10 Utility Theory
Measuring Utility and Constructing a Utility Curve
Utility as a Decision-Making Criterion
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Starting Right Corporation
Case Study: Blake Electronics
Bibliography
CHAPTER 4 Regression Models
4.1 Introduction
4.2 Scatter Diagrams
4.3 Simple Linear Regression
4.4 Measuring the Fit of the Regression Model
Coefficient of Determination
Correlation Coefficient
4.5 Assumptions of the Regression Model
Estimating the Variance
4.6 Testing the Model for Significance
Triple A Construction Example
The Analysis of Variance (ANOVA) Table
Triple A Construction ANOVA Example
4.7 Using Computer Software for Regression
Excel 2013
Excel QM
QM for Windows
4.8 Multiple Regression Analysis
Evaluating the Multiple Regression Model
Jenny Wilson Realty Example
4.9 Binary or Dummy Variables
4.10 Model Building
Stepwise Regression
Multicollinearity
4.11 Nonlinear Regression
4.12 Cautions and Pitfalls in Regression Analysis
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: North–South Airline
Bibliography
Appendix 4.1: Formulas for Regression Calculations
CHAPTER 5 Forecasting
5.1 Introduction
5.2 Types of Forecasting Models
Qualitative Models
Causal Models
Time-Series Models
5.3 Components of a Time-Series
5.4 Measures of Forecast Accuracy
5.5 Forecasting Models—Random Variations Only
Moving Averages
Weighted Moving Averages
Exponential Smoothing
Using Software for Forecasting Time Series
5.6 Forecasting Models—Trend and Random Variations
Exponential Smoothing with Trend
Trend Projections
5.7 Adjusting for Seasonal Variations
Seasonal Indices
Calculating Seasonal Indices with No Trend
Calculating Seasonal Indices with Trend
5.8 Forecasting Models—Trend, Seasonal, and Random Variations
The Decomposition Method
Software for Decomposition
Using Regression with Trend and Seasonal Components
5.9 Monitoring and Controlling Forecasts
Adaptive Smoothing
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Forecasting Attendance at SWU Football Games
Case Study: Forecasting Monthly Sales
Bibliography
CHAPTER 6 Inventory Control Models
6.1 Introduction
6.2 Importance of Inventory Control
Decoupling Function
Storing Resources
Irregular Supply and Demand
Quantity Discounts
Avoiding Stockouts and Shortages
6.3 Inventory Decisions
6.4 Economic Order Quantity: Determining How Much to Order
Inventory Costs in the EOQ Situation
Finding the EOQ
Sumco Pump Company Example
Purchase Cost of Inventory Items
Sensitivity Analysis with the EOQ Model
6.5 Reorder Point: Determining When to Order
6.6 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
6.7 Quantity Discount Models
Brass Department Store Example
6.8 Use of Safety Stock
6.9 Single-Period Inventory Models
Marginal Analysis with Discrete Distributions
Café du Donut Example
Marginal Analysis with the Normal Distribution
Newspaper Example
6.10 ABC Analysis
6.11 Dependent Demand: The Case for Material Requirements Planning
Material Structure Tree
Gross and Net Material Requirements Plan
Two or More End Products
6.12 Just-In-Time Inventory Control
6.13 Enterprise Resource Planning
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Martin-Pullin Bicycle Corporation
Bibliography
Appendix 6.1: Inventory Control with QM for Windows
CHAPTER 7 Linear Programming Models: Graphical and Computer Methods
7.1 Introduction
7.2 Requirements of a Linear Programming Problem
7.3 Formulating LP Problems
Flair Furniture Company
7.4 Graphical Solution to an LP Problem
Graphical Representation of Constraints
Isoprofit Line Solution Method
Corner Point Solution Method
Slack and Surplus
7.5 Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2013, and Excel QM
Using QM for Windows
Using Excel’s Solver Command to Solve LP Problems
Using Excel QM
7.6 Solving Minimization Problems
Holiday Meal Turkey Ranch
7.7 Four Special Cases in LP
No Feasible Solution
Unboundedness
Redundancy
Alternate Optimal Solutions
7.8 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
Case Study: Mexicana Wire Works
Bibliography
CHAPTER 8 Linear Programming Applications
8.1 Introduction
8.2 Marketing Applications
Media Selection
Marketing Research
8.3 Manufacturing Applications
Production Mix
Production Scheduling
8.4 Employee Scheduling Applications
Labor Planning
8.5 Financial Applications
Portfolio Selection
Truck Loading Problem
8.6 Ingredient Blending Applications
Diet Problems
Ingredient Mix and Blending Problems
8.7 Other Linear Programming Applications
Summary
Self-Test
Problems
Case Study: Cable & Moore
Bibliography
CHAPTER 9 Transportation, Assignment, and Network Models
9.1 Introduction
9.2 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
9.3 The Assignment Problem
Linear Program for Assignment Example
9.4 The Transshipment Problem
Linear Program for Transshipment Example
9.5 Maximal-Flow Problem
Example
9.6 Shortest-Route Problem
9.7 Minimal-Spanning Tree Problem
Summary
Glossary
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Andrew–Carter, Inc.
Case Study: Northeastern Airlines
Case Study: Southwestern University Traffic Problems
Bibliography
Appendix 9.1: Using QM for Windows
CHAPTER 10 Integer Programming, Goal Programming, and Nonlinear Programming
10.1 Introduction
10.2 Integer Programming
Harrison Electric Company Example of Integer Programming
Using Software to Solve the Harrison Integer Programming Problem
Mixed-Integer Programming Problem Example
10.3 Modeling with 0–1 (Binary) Variables
Capital Budgeting Example
Limiting the Number of Alternatives Selected
Dependent Selections
Fixed-Charge Problem Example
Financial Investment Example
10.4 Goal Programming
Example of Goal Programming: Harrison Electric Company Revisited
Extension to Equally Important Multiple Goals
Ranking Goals with Priority Levels
Goal Programming with Weighted Goals
10.5 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
Self-Test
Discussion Questions and Problems
Case Study: Schank Marketing Research
Case Study: Oakton River Bridge
Bibliography
CHAPTER 11 Project Management
11.1 Introduction
11.2 PERT/CPM
General Foundry Example of PERT/CPM
Drawing the PERT/CPM Network
Activity Times
How to Find the Critical Path
Probability of Project Completion
What PERT Was Able to Provide
Using Excel QM for the General Foundry Example
Sensitivity Analysis and Project Management
11.3 PERT/Cost
Planning and Scheduling Project Costs: Budgeting Process
Monitoring and Controlling Project Costs
11.4 Project Crashing
General Foundary Example
Project Crashing with Linear Programming
11.5 Other Topics in Project Management
Subprojects
Milestones
Resource Leveling
Software
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Southwestern University Stadium Construction
Case Study: Family Planning Research Center of Nigeria
Bibliography
Appendix 11.1: Project Management with QM for Windows
CHAPTER 12 Waiting Lines and Queuing Theory Models
12.1 Introduction
12.2 Waiting Line Costs
Three Rivers Shipping Company Example
12.3 Characteristics of a Queuing System
Arrival Characteristics
Waiting Line Characteristics
Service Facility Characteristics
Identifying Models Using Kendall Notation
12.4 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
Enhancing the Queuing Environment
12.5 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m)
Equations for the Multichannel Queuing Model
Arnold’s Muffler Shop Revisited
12.6 Constant Service Time Model (M/D/1)
Equations for the Constant Service Time Model
Garcia-Golding Recycling, Inc.
12.7 Finite Population Model (M/M/1 with Finite Source)
Equations for the Finite Population Model
Department of Commerce Example
12.8 Some General Operating Characteristic Relationships
12.9 More Complex Queuing Models and the Use of Simulation
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: New England Foundry
Case Study: Winter Park Hotel
Bibliography
Appendix 12.1: Using QM for Windows
CHAPTER 13 Simulation Modeling
13.1 Introduction
13.2 Advantages and Disadvantages of Simulation
13.3 Monte Carlo Simulation
Harry’s Auto Tire Example
Using QM for Windows for Simulation
Simulation with Excel Spreadsheets
13.4 Simulation and Inventory Analysis
Simkin’s Hardware Store
Analyzing Simkin’s Inventory Costs
13.5 Simulation of a Queuing Problem
Port of New Orleans
Using Excel to Simulate the Port of New Orleans Queuing Problem
13.6 Simulation Model for a Maintenance Policy
Three Hills Power Company
Cost Analysis of the Simulation
13.7 Other Simulation Issues
Two Other Types of Simulation Models
Verification and Validation
Role of Computers in Simulation
Summary
Glossary
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Alabama Airlines
Case Study: Statewide Development Corporation
Case Study: FB Badpoore Aerospace
Bibliography
CHAPTER 14 Markov Analysis
14.1 Introduction
14.2 States and State Probabilities
The Vector of State Probabilities for Three Grocery Stores Example
14.3 Matrix of Transition Probabilities
Transition Probabilities for the Three Grocery Stores
14.4 Predicting Future Market Shares
14.5 Markov Analysis of Machine Operations
14.6 Equilibrium Conditions
14.7 Absorbing States and the Fundamental Matrix: Accounts Receivable Application
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Case Study: Rentall Trucks
Bibliography
Appendix 14.1: Markov Analysis with QM for Windows
Appendix 14.2: Markov Analysis With Excel
CHAPTER 15 Statistical Quality Control
15.1 Introduction
15.2 Defining Quality and TQM
15.3 Statiscal Process Control
Variability in the Process
15.4 Control Charts for Variables
The Central Limit Theorem
Setting x-Chart Limits
Setting Range Chart Limits
15.5 Control Charts for Attributes
p-Charts
c-Charts
Summary
Glossary
Key Equations
Solved Problems
Self-Test
Discussion Questions and Problems
Bibliography
Appendix 15.1: Using QM for Windows for SPC
APPENDICES
APPENDIX A Areas Under the Standard
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

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