book

Quantitative Analysis for Management, 13/e

Name: Quantitative Analysis for Management, 13/e
ISBN: 9780134543161

by Barry Render, Ralph M. Stair, Michael E. Hanna, Trevor S. Hale

January 2017

Beginner to intermediate

280 pages

217h 11m

English

Pearson

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Quantitative Analysis for Management
About the Authors
Brief Contents
Contents
Preface
OverviewSpecial FeaturesSignificant Changes to the Thirteenth EditionOnline ModulesSoftwareCompanion WebsiteInstructor 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 ProblemDeveloping a ModelAcquiring Input DataDeveloping a SolutionTesting the SolutionAnalyzing the Results and Sensitivity AnalysisImplementing the ResultsThe Quantitative Analysis Approach and Modeling in the Real World
1.4 How to Develop a Quantitative Analysis Model
The Advantages of Mathematical ModelingMathematical 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 ProblemConflicting ViewpointsImpact on Other DepartmentsBeginning AssumptionsSolution OutdatedDeveloping a ModelFitting the Textbook ModelsUnderstanding the ModelAcquiring Input DataUsing Accounting DataValidity of DataDeveloping a SolutionHard-to-Understand MathematicsOnly One Answer is LimitingTesting the SolutionAnalyzing the Results
1.7 Implementation—Not Just the Final Step
Lack of Commitment and Resistance to ChangeLack of Commitment by Quantitative Analysts
Summary
Glossary
Key Equations
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
Bibliography
Chapter 2 Probability Concepts and Applications
Learning Objectives
2.1 Fundamental Concepts
Two Basic Rules of ProbabilityTypes of ProbabilityMutually Exclusive and Collectively Exhaustive EventsUnions and Intersections of EventsProbability Rules for Unions, Intersections, and Conditional Probabilities
2.2 Revising 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 VariableExpected Value of a Discrete Probability DistributionVariance of a Discrete Probability DistributionProbability Distribution of a Continuous Random Variable
2.6 The Binomial Distribution
Solving Problems with the Binomial FormulaSolving Problems with Binomial Tables
2.7 The Normal Distribution
Area Under the Normal CurveUsing the Standard Normal TableHaynes Construction Company ExampleThe 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’ 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
OptimisticPessimistic 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
SolutionSolution 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 DeterminationCorrelation Coefficient
4.4 Assumptions of the Regression Model
Estimating the Variance
4.5 Testing the Model for Significance
Triple A Construction ExampleThe Analysis of Variance (ANOVA) TableTriple A Construction ANOVA Example
4.6 Using Computer Software for Regression
Excel 2016Excel QMQM for Windows
4.7 Multiple Regression Analysis
Evaluating the Multiple Regression ModelJenny Wilson Realty Example
4.8 Binary or Dummy Variables
4.9 Model Building
Stepwise RegressionMulticollinearity
4.10 Nonlinear Regression
4.11 Cautions and Pitfalls in Regression Analysis
Summary
Glossary
Key Equations
Solved Problems
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
Bibliography
Appendix 4.1: Formulas for Regression Calculations
Chapter 5 Forecasting
Learning Objectives
5.1 Types of Forecasting Models
Qualitative ModelsCausal ModelsTime-Series Models
5.2 Components of a Time-Series
5.3 Measures of Forecast Accuracy
5.4 Forecasting Models—Random Variations Only
Moving AveragesWallace Garden Supply ExampleWeighted Moving AveragesExponential SmoothingGetting StartedPort of Baltimore ExampleUsing Software for Forecasting Time Series
5.5 Forecasting Models—Trend and Random Variations
Exponential Smoothing with TrendTrend Projections
5.6 Adjusting for Seasonal Variations
Seasonal IndicesCalculating Seasonal Indices with No TrendCalculating Seasonal Indices with Trend
5.7 Forecasting Models—Trend, Seasonal, and Random Variations
The Decomposition MethodSoftware for DecompositionUsing Regression with Trend and Seasonal Components
5.8 Monitoring and Controlling Forecasts
Adaptive Smoothing
Summary
Glossary
Key Equations
Solved Problems
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsBibliography
Chapter 6 Inventory Control Models
Learning Objectives
6.1 Importance of Inventory Control
Decoupling FunctionStoring ResourcesIrregular Supply and DemandQuantity DiscountsAvoiding Stockouts and Shortages
6.2 Inventory Decisions
6.3 Economic Order Quantity: Determining How Much to Order
Inventory Costs in the EOQ SituationFinding the EOQSumco Pump Company ExampleUsing Excel QM for Basic EOQ Inventory ProblemsPurchase Cost of Inventory ItemsSensitivity 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 ModelAnnual Setup Cost or Annual Ordering CostDetermining the Optimal Production QuantityBrown Manufacturing ExampleUsing Excel QM for Production Run Models
6.6 Quantity Discount Models
Brass Department Store ExampleUsing Excel QM for Quantity Discount Problems
6.7 Use of Safety Stock
6.8 Single-Period Inventory Models
Marginal Analysis with Discrete DistributionsCafé du Donut ExampleMarginal Analysis with the Normal DistributionNewspaper Example
6.9 ABC Analysis
6.10 Dependent Demand: The Case for Material Requirements Planning
Material Structure TreeGross and Net Material Requirements PlansTwo or More End Products
6.11 Just-In-Time Inventory Control
6.12 Enterprise Resource Planning
Summary
Glossary
Key Equations
Solved Problems
SolutionSolutionSolutionSolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsBibliographyAppendix 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 ConstraintsIsoprofit 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 QM
Using QM for WindowsUsing Excel’s Solver Command to Solve LP ProblemsPreparing the Spreadsheet For SolverUsing SolverUsing Excel QM
7.5 Solving Minimization Problems
Holiday Meal Turkey RanchUsing The Corner Point Method On A Minimization ProblemIsocost Line Approach
7.6 Four Special Cases in LP
No Feasible SolutionUnboundednessRedundancyAlternate Optimal Solutions
7.7 Sensitivity Analysis
High Note Sound CompanyChanges in the Objective Function CoefficientQM for Windows and Changes in Objective Function CoefficientsExcel Solver and Changes in Objective Function CoefficientsChanges in the Technological CoefficientsChanges in the Resources or Right-Hand-Side ValuesQM for Windows and Changes in Right-Hand-Side ValuesExcel Solver and Changes in Right-Hand-Side Values
Summary
Glossary
Solved Problems
SolutionSolutionSolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsDiscussion QuestionsBibliography
Chapter 8 Linear Programming Applications
Learning Objectives
8.1 Marketing Applications
Media SelectionMarketing Research
8.2 Manufacturing Applications
Production MixProduction Scheduling
8.3 Employee Scheduling Applications
Labor Planning
8.4 Financial Applications
Portfolio SelectionTruck Loading Problem
8.5 Ingredient Blending Applications
Diet ProblemsIngredient 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 ExampleSolving Transportation Problems Using Computer SoftwareA General LP Model for Transportation ProblemsFacility 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 QuestionsProblems*BibliographyAppendix 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 ProgrammingUsing Software to Solve the Harrison Integer Programming ProblemMixed-Integer Programming Problem ExampleUsing QM For Windows And Excel to Solve Bagwell’s Integer Programming Model
10.2 Modeling with 0–1 (Binary) Variables
Capital Budgeting ExampleLimiting the Number of Alternatives SelectedDependent SelectionsFixed-Charge Problem ExampleFinancial Investment Example
10.3 Goal Programming
Extension to Equally Important Multiple GoalsRanking Goals with Priority LevelsGoal Programming with Weighted GoalsUsing QM For Windows To Solve Harrison’s Problem
10.4 Nonlinear Programming
Nonlinear Objective Function and Linear ConstraintsBoth Nonlinear Objective Function and Nonlinear ConstraintsLinear Objective Function with Nonlinear Constraints
Summary
Glossary
Solved Problems
SolutionSolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsBibliography
Chapter 11 Project Management
Learning Objectives
11.1 PERT/CPM
General Foundry Example of PERT/CPMDrawing the PERT/CPM NetworkActivity TimesHow to Find the Critical PathEarliest TimesLatest TimesConcept Of Slack In Critical Path ComputationsProbability of Project CompletionWhat PERT Was Able to ProvideUsing Excel QM for the General Foundry ExampleSensitivity Analysis and Project Management
11.2 PERT/Cost
Planning and Scheduling Project Costs: Budgeting ProcessBudgeting For General FoundryMonitoring and Controlling Project Costs
11.3 Project Crashing
General Foundry ExampleProject Crashing with Linear ProgrammingObjective FunctionCrash Time ConstraintsProject Completion ConstraintConstraints Describing The Network
11.4 Other Topics in Project Management
SubprojectsMilestonesResource LevelingSoftware
Summary
Glossary
Key Equations
Solved Problems
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsDiscussion 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 CharacteristicsSize Of The Calling PopulationPattern Of Arrivals At The SystemBehavior Of The ArrivalsWaiting Line CharacteristicsService Facility CharacteristicsBasic Queuing System ConfigurationsService Time DistributionIdentifying Models Using Kendall Notation
12.3 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M /1)
Assumptions of the ModelQueuing EquationsArnold’s Muffler Shop CaseUsing Excel QM On The Arnold’s Muffler Shop QueueIntroducing Costs Into The ModelEnhancing the Queuing Environment
12.4 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M ∕M ∕m)
Equations for the Multichannel Queuing ModelArnold’s Muffler Shop RevisitedUsing 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 ModelGarcia-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 ModelDepartment of Commerce ExampleSolving 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
SolutionSolutionSolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
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 ExampleStep 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 SimulationSimulation with Excel Spreadsheets
13.3 Simulation and Inventory Analysis
Simkin’s Hardware StoreAnalyzing Simkin’s Inventory Costs
13.4 Simulation of a Queuing Problem
Port of New OrleansUsing Excel to Simulate the Port of New Orleans Queuing Problem
13.5 Simulation Model for a Maintenance Policy
Three Hills Power CompanyColumn 1: Breakdown NumberColumn 2: Random Number for BreakdownsColumn 3: Time Between BreakdownsColumn 4: Time of BreakdownColumn 5: Time Repairperson Is Free to Begin RepairColumn 6: Random Number for Repair TimeColumn 7: Repair Time RequiredColumn 8: Time Repair EndsColumn 9: Number of Hours the Machine Is DownCost Analysis of the SimulationBuilding An Excel Simulation Model For Three Hills Power Company
13.6 Other Simulation Issues
Two Other Types of Simulation ModelsOperational GamingSystems SimulationVerification and ValidationRole of Computers in Simulation
Summary
Glossary
Solved Problems
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
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
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
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 ProcessBuilding Control ChartsNatural VariationsAssignable Variations
15.3 Control Charts for Variables
The Central Limit TheoremSetting X -Chart LimitsBox-Filling ExampleUsing Excel QM For Box-Filling ExampleSuper Cola ExampleUsing Excel QM For Super Cola ExampleSetting Range Chart LimitsRange Example
15.4 Control Charts for Attributes
p-ChartsARCO p-Chart ExampleUsing Excel QM For p-Chartsc-ChartsRed Top Cab Company c-Chart ExampleUsing Excel QM For c-Charts
Summary
Glossary
Key Equations
Solved Problems
SolutionSolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
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 DecisionProbability Distribution of DemandUsing Expected Monetary Value to Make a Decision
M3.2 Expected Value of Perfect Information and the Normal Distribution
Opportunity Loss FunctionExpected Opportunity Loss
Summary
Glossary
Key Equations
Solved Problems
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblems
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
SolutionSolution
Self-Test
Discussion Questions and Problems
Discussion QuestionsProblemsBibliography
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

Content preview from Quantitative Analysis for Management, 13/e

2.2 Revising Probabilities with Bayes’ Theorem

Bayes’ Theorem is used to incorporate additional information as it is made available and help create revised or posterior probabilities from the original or prior probabilities. This means that we can take new or recent data and then revise and improve upon our old probability estimates for an event (see Figure 2.3). Let us consider the following example.

A flowchart showing that when prior probabilities and new information are reviewed with Bayes’ process, it leads to revised posterior probabilities. — Figure 2.3 Using Bayes’ Process

Figure 2.3 Full Alternative Text

A cup contains two dice identical in appearance. One, however, is fair (unbiased) and the other is loaded (biased). The probability of rolling a 3 on the fair die is $1 /_{6},$ or 0.166. ...

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