## Description

### 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

## Reviews

There are no reviews yet.