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Quantitative Methods for Business With Infotrac

by
Edition:
10th
ISBN13:

9780324312652

ISBN10:
0324312652
Format:
Hardcover
Pub. Date:
10/17/2005
Publisher(s):
South-Western College Pub
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Summary

This revision of QUANTITATIVE METHODS FOR BUSINESS provides students with a conceptual understanding of the role that quantitative methods play in the decision-making process. This text describes the many quantitative methods that have been developed over the years, explains how they work, and shows how the decision-maker can apply and interpret data. Written with the non-mathematician in mind, this text is applications-oriented. Its "Problem-Scenario Approach" motivates and helps students understand and apply mathematical concepts and techniques. In addition, the managerial orientation motivates students by using examples that illustrate situations in which quantitative methods are useful in decision making.

Table of Contents

Preface xiii
Introduction
1(28)
Problem Solving and Decision Making
3(1)
Quantitative Analysis and Decision Making
4(2)
Quantitative Analysis
6(7)
Model Development
7(3)
Data Preparation
10(1)
Model Solution
11(1)
Report Generation
12(1)
A Note Regarding Implementation
12(1)
Models of Cost, Revenue, and Profit
13(3)
Cost and Volume Models
14(1)
Revenue and Volume Models
14(1)
Profit and Volume Models
15(1)
Breakeven Analysis
15(1)
Quantitative Methods in Practice
16(13)
Methods Used Most Frequently
17(1)
Summary
18(1)
Glossary
18(1)
Problems
19(4)
Case Problem: Scheduling a Golf League
23(1)
Appendix 1.1 The Management Scientist Software
23(2)
Appendix 1.2 Using Excel for Breakeven Analysis
25(4)
Introduction to Probability
29(30)
Experiments and the Sample Space
31(1)
Assigning Probabilities to Experimental Outcomes
32(2)
Classical Method
32(1)
Relative Frequency Method
33(1)
Subjective Method
33(1)
Events and Their Probabilities
34(1)
Some Basic Relationships of Probability
34(9)
Complement of an Event
35(1)
Addition Law
36(2)
Conditional Probability
38(4)
Multiplication Law
42(1)
Bayes' Theorem
43(16)
The Tabular Approach
47(1)
Summary
48(1)
Glossary
49(1)
Problems
50(6)
Case Problem: Hamilton County Judges
56(3)
Probability Distributions
59(37)
Random Variables
60(1)
Discrete Random Variables
61(4)
Probability Distribution of a Discrete Random Variable
62(1)
Expected Value
63(1)
Variance
64(1)
Binomial Probability Distribution
65(5)
Nastke Clothing Store Problem
66(2)
Expected Value and Variance for the Binomial Distribution
68(2)
Poisson Probability Distribution
70(2)
An Example Involving Time Intervals
70(1)
An Example Involving Length or Distance Intervals
71(1)
Continuous Random Variables
72(3)
Applying the Uniform Distribution
72(1)
Area as a Measure of Probability
73(2)
Normal Probability Distribution
75(8)
Standard Normal Distribution
76(1)
Computing Probabilities for Any Normal Distribution
77(4)
Grear Tire Company Problem
81(2)
Exponential Probability Distribution
83(13)
Computing Probabilities for the Exponential Distribution
84(1)
Relationship Between the Poisson and Exponential Distributions
84(1)
Summary
85(1)
Glossary
85(1)
Problems
86(6)
Case Problem: Specialty Toys
92(1)
Appendix 3.1 Computing Discrete Probabilities with Excel
93(1)
Appendix 3.2 Computing Probabilities for Continuous Distributions with Excel
94(2)
Decision Analysis
96(57)
Problem Formulation
98(3)
Influence Diagrams
99(1)
Payoff Tables
99(1)
Decision Trees
99(2)
Decision Making Without Probabilities
101(2)
Optimistic Approach
101(1)
Conservative Approach
101(1)
Minimax Regret Approach
102(1)
Decision Making with Probabilities
103(5)
Expected Value of Perfect Information
106(2)
Risk Analysis and Sensitivity Analysis
108(5)
Risk Analysis
108(1)
Sensitivity Analysis
108(5)
Decision Analysis with Sample Information
113(11)
Influence Diagram
113(1)
Decision Tree
114(2)
Decision Strategy
116(4)
Risk Profile
120(2)
Expected Value of Sample Information
122(1)
Efficiency of Sample Information
123(1)
Computing Branch Probabilities
124(29)
Summary
128(1)
Glossary
129(2)
Problems
131(13)
Case Problem 1: Property Purchase Strategy
144(1)
Case Problem 2: Lawsuit Defense Strategy
145(1)
Appendix 4.1 Decision Analysis with TreePlan
146(7)
Utility and Game Theory
153(26)
The Meaning of Utility
154(1)
Utility and Decision Making
155(5)
The Expected Utility Approach
158(1)
Summary of Steps for Determining the Utility of Money
159(1)
Utility: Other Considerations
160(4)
Risk Avoiders Versus Risk Takers
160(3)
Expected Monetary Value Versus Expected Utility
163(1)
Introduction to Game Theory
164(3)
Competing for Market Share
165(1)
Identifying a Pure Strategy
166(1)
Mixed Strategy Games
167(12)
A Larger Mixed Strategy Game
170(1)
Summary of Steps for Solving Two-Person, Zero-Sum Games
171(1)
Extensions
171(1)
Summary
172(1)
Glossary
172(1)
Problems
173(6)
Forecasting
179(52)
Components of a Time Series
182(2)
Trend Component
182(1)
Cyclical Component
183(1)
Seasonal Component
184(1)
Irregular Component
184(1)
Smoothing Methods
184(9)
Moving Averages
184(3)
Weighted Moving Averages
187(1)
Exponential Smoothing
188(5)
Trend Projection
193(3)
Trend and Seasonal Components
196(9)
Multiplicative Model
196(1)
Calculating the Seasonal Indexes
197(4)
Deseasonalizing the Time Series
201(1)
Using Deseasonalized Time Series to Identify Trend
201(3)
Seasonal Adjustments
204(1)
Models Based on Monthly Data
205(1)
Cyclical Component
205(1)
Regression Analysis
205(7)
Using Regression Analysis as a Causal Forecasting Method
206(5)
Using Regression Analysis with Time Series Data
211(1)
Qualitative Approaches
212(19)
Delphi Method
213(1)
Expert Judgment
213(1)
Scenario Writing
213(1)
Intuitive Approaches
213(1)
Summary
213(1)
Glossary
214(1)
Problems
215(9)
Case Problem 1: Forecasting Sales
224(1)
Case Problem 2: Forecasting Lost Sales
225(1)
Appendix 6.1 Using Excel for Forecasting
226(1)
Appendix 6.2 Using CB Predictor for Forecasting
227(4)
Introduction to Linear Programming
231(59)
A Simple Maximization Problem
233(4)
Problem Formulation
234(2)
Mathematical Model for the RMC Problem
236(1)
Graphical Solution Procedure
237(12)
A Note on Graphing Lines
246(1)
Summary of the Graphical Solution Procedure for Maximization Problems
247(1)
Slack Variables
248(1)
Extreme Points and the Optimal Solution
249(2)
Computer Solution of the RMC Problem
251(2)
Interpretation of Computer Output
252(1)
A Simple Minimization Problem
253(6)
Summary of the Graphical Solution Procedure for Minimization Problems
256(1)
Surplus Variables
256(2)
Computer Solution of the M&D Chemicals Problem
258(1)
Special Cases
259(4)
Alternative Optimal Solutions
259(1)
Infeasibility
260(1)
Unbounded
261(2)
General Linear Programming Notation
263(27)
Summary
264(2)
Glossary
266(1)
Problems
267(14)
Case Problem 1: Workload Balancing
281(1)
Case Problem 2: Production Strategy
282(1)
Case Problem 3: Hart Venture Capital
283(1)
Appendix 7.1 Solving Linear Programs with The Management Scientist
283(1)
Appendix 7.2 Solving Linear Programs with LINDO®
284(2)
Appendix 7.3 Solving Linear Programs with Excel
286(4)
Linear Programming: Sensitivity Analysis and Interpretation of Solution
290(58)
Introduction to Sensitivity Analysis
292(1)
Objective Function Coefficients
293(5)
Simultaneous Changes
296(2)
Right-Hand Sides
298(7)
Simultaneous Changes
301(1)
A Second Example
302(2)
Cautionary Note on the Interpretation of Dual Prices
304(1)
More Than Two Decision Variables
305(9)
Modified RMC Problem
305(6)
Bluegrass Farms Problem
311(3)
Electronic Communications Problem
314(34)
Problem Formulation
315(1)
Computer Solution and Interpretation
316(3)
Summary
319(1)
Glossary
320(1)
Problems
321(21)
Case Problem 1: Product Mix
342(1)
Case Problem 2: Investment Strategy
343(1)
Case Problem 3: Truck Leasing Strategy
344(1)
Appendix 8.1 Sensitivity Analysis with Excel
345(3)
Linear Programming Applications
348(76)
Marketing Applications
349(6)
Media Selection
350(3)
Marketing Research
353(2)
Financial Applications
355(8)
Portfolio Selection
355(4)
Financial Planning
359(4)
Production Management Applications
363(15)
A Make-or-Buy Decision
363(4)
Production Scheduling
367(5)
Workforce Assignment
372(6)
Blending Problems
378(5)
Data Envelopment Analysis
383(8)
Evaluating the Performance of Hospitals
384(1)
Overview of the DEA Approach
385(1)
DEA Linear Programming Model
385(5)
Summary of the DEA Approach
390(1)
Revenue Management
391(33)
Summary
396(1)
Glossary
396(1)
Problems
397(16)
Case Problem 1: Planning an Advertising Campaign
413(1)
Case Problem 2: Phoenix Computer
414(1)
Case Problem 3: Textile Mill Scheduling
415(1)
Case Problem 4: Workforce Scheduling
416(1)
Case Problem 5: Cinergy Coal Allocation
417(3)
Appendix 9.1 Excel Solution of Hewlitt Corporation Financial Planning Problem
420(4)
Transportation, Assignment, and Transshipment Problems
424(48)
The Transportation Problem: The Network Model and a Linear Programming Formulation
425(7)
Problem Variations
428(3)
A General Linear Programming Model of the Transportation Problem
431(1)
The Assignment Problem: The Network Model and a Linear Programming Formulation
432(6)
Problem Variations
435(1)
A General Linear Programming Model of the Assignment Problem
436(1)
Multiple Assignments
436(2)
The Transshipment Problem: The Network Model and a Linear Programming Formulation
438(7)
Problem Variations
443(1)
A General Linear Programming Model of the Transshipment Problem
444(1)
A Production and Inventory Application
445(27)
Summary
448(1)
Glossary
449(1)
Problems
449(14)
Case Problem 1: Solutions Plus
463(1)
Case Problem 2: Distribution System Design
464(2)
Appendix 10.1 Excel Solution of Transportation, Assignment, and Transshipment Problems
466(6)
Integer Linear Programming
472(45)
Types of Integer Linear Programming Models
474(2)
Graphical and Computer Solutions for an All-Integer Linear Program
476(4)
Graphical Solution of the LP Relaxation
476(1)
Rounding to Obtain an Integer Solution
477(1)
Graphical Solution of the All-Integer Problem
478(1)
Using the LP Relaxation to Establish Bounds
479(1)
Computer Solution
479(1)
Application Involving 0-1 Variables
480(16)
Capital Budgeting
480(2)
Fixed Cost
482(2)
Distribution System Design
484(5)
Bank Location
489(2)
Product Design and Market Share Optimization
491(5)
Modeling Flexibility Provided by 0-1 Integer Variables
496(21)
Multiple-Choice and Mutually Exclusive Constraints
496(1)
k Out of n Alternatives Constraint
497(1)
Conditional and Corequisite Constraints
497(1)
A Cautionary Note About Sensitivity Analysis
497(2)
Summary
499(1)
Glossary
499(1)
Problems
500(11)
Case Problem 1: Textbook Publishing
511(1)
Case Problem 2: Yeager National Bank
512(1)
Case Problem 3: Production Scheduling with Changeover Costs
513(1)
Appendix 11.1 Excel Solution of Integer Linear Programs
514(3)
Project Scheduling: PERT/CPM
517(37)
Project Scheduling with Known Activity Times
518(10)
The Concept of a Critical Path
519(2)
Determining the Critical Path
521(4)
Contributions of PERT/CPM
525(1)
Summary of the PERT/CPM Critical Path Procedure
526(2)
Project Scheduling with Uncertain Activity Times
528(8)
The Daugherty Porta-Vac Project
528(1)
Uncertain Activity Times
529(2)
The Critical Path
531(2)
Variability in Project Completion Time
533(3)
Considering Time-Cost Trade-Offs
536(18)
Crashing Activity Times
536(3)
Linear Programming Model for Crashing
539(2)
Summary
541(1)
Glossary
542(1)
Problems
542(10)
Case Problem: R. C. Coleman
552(2)
Inventory Models
554(47)
Economic Order Quantity (EOQ) Model
555(9)
The How-Much-to-Order Decision
559(2)
The When-to-Order Decision
561(1)
Sensitivity Analysis for the EOQ Model
562(1)
Excel Solution of the EOQ Model
563(1)
Summary of the EOQ Model Assumptions
563(1)
Economic Production Lot Size Model
564(4)
Total Cost Model
565(2)
Economic Production Lot Size
567(1)
Inventory Model with Planned Shortages
568(4)
Quantity Discounts for the EOQ Model
572(2)
Single-Period Inventory Model with Probabilistic Demand
574(6)
Johnson Shoe Company
575(3)
Nationwide Car Rental
578(2)
Order-Quantity, Reorder Point Model with Probabilistic Demand
580(4)
The How-Much-to-Order Decision
581(1)
The When-to-Order Decision
582(2)
Periodic Review Model with Probabilistic Demand
584(17)
More Complex Periodic Review Models
587(1)
Summary
588(1)
Glossary
589(1)
Problems
590(7)
Case Problem 1: Wagner Fabricating Company
597(1)
Case Problem 2: River City Fire Department
598(1)
Appendix 13.1 Development of the Optimal Order Quantity (Q) Formula for the EOQ Model
599(1)
Appendix 13.2 Development of the Optimal Lot Size (Q*) Formula for the Production Lot Size Model
600(1)
Waiting Line Models
601(40)
Structure of a Waiting Line System
603(4)
Single-Channel Waiting Line
603(1)
Distribution of Arrivals
603(2)
Distribution of Service Times
605(1)
Queue Discipline
606(1)
Steady-State Operation
606(1)
Single-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times
607(4)
Operating Characteristics
607(1)
Operating Characteristics for the Burger Dome Problem
608(1)
Managers' Use of Waiting Line Models
609(1)
Improving the Waiting Line Operation
609(1)
Excel Solution of Waiting Line Model
610(1)
Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times
611(5)
Operating Characteristics
612(2)
Operating Characteristics for the Burger Dome Problem
614(2)
Some General Relationships for Waiting Line Models
616(2)
Economic Analysis of Waiting Lines
618(1)
Other Waiting Line Models
619(1)
Single-Channel Waiting Line Model with Poisson Arrivals and Arbitrary Service Times
620(3)
Operating Characteristics for the M/G/1 Model
620(2)
Constant Service Times
622(1)
Multiple-Channel Model with Poisson Arrivals, Arbitrary Service Times, and No Waiting Line
623(2)
Operating Characteristics for the M/G/k Model with Blocked Customers Cleared
623(2)
Waiting Line Models with Finite Calling Populations
625(16)
Operating Characteristics for the M/M/1 Model with a Finite Calling Population
626(2)
Summary
628(2)
Glossary
630(1)
Problems
630(8)
Case Problem 1: Regional Airlines
638(1)
Case Problem 2: Office Equipment, Inc.
639(2)
Simulation
641(59)
Risk Analysis
644(13)
PortaCom Project
644(1)
What-If Analysis
644(2)
Simulation
646(7)
Simulation of the PortaCom Problem
653(4)
Inventory Simulation
657(5)
Simulation of the Butler Inventory Problem
660(2)
Waiting Line Simulation
662(11)
Hammondsport Savings Bank ATM Waiting Line
662(1)
Customer Arrival Times
663(1)
Customer Service Times
664(1)
Simulation Model
664(4)
Simulation of the Hammondsport Savings Bank ATM Problem
668(1)
Simulation with Two ATMs
669(2)
Simulation Results with Two ATMs
671(2)
Other Simulation Issues
673(27)
Computer Implementation
673(1)
Verification and Validation
674(1)
Advantages and Disadvantages of Using Simulation
674(1)
Summary
675(1)
Glossary
676(1)
Problems
677(7)
Case Problem 1: Tri-State Corporation
684(2)
Case Problem 2: Harbor Dunes Golf Course
686(1)
Case Problem 3: County Beverage Drive-Thru
687(2)
Appendix 15.1 Simulation with Excel
689(6)
Appendix 15.2 Simulation Using Crystal Ball
695(5)
Markov Processes
700(25)
Market Share Analysis
702(7)
Accounts Receivable Analysis
709(16)
Fundamental Matrix and Associated Calculations
711(1)
Establishing the Allowance for Doubtful Accounts
712(2)
Summary
714(1)
Glossary
715(1)
Problems
715(4)
Case Problem: Dealer's Absorbing State Probabilities in Blackjack
719(1)
Appendix 16.1 Matrix Notation and Operations
720(4)
Appendix 16.2 Matrix Inversion with Excel
724(1)
Multicriteria Decisions
725(43)
Goal Programming: Formulation and Graphical Solution
726(8)
Developing the Constraints and the Goal Equations
727(2)
Developing an Objective Function with Preemptive Priorities
729(1)
Graphical Solution Procedure
729(3)
Goal Programming Model
732(2)
Goal Programming: Solving More Complex Problems
734(5)
Suncoast Office Supplies Problem
734(1)
Formulating the Goal Equations
735(1)
Formulating the Objective Function
736(1)
Computer Solution
737(2)
Scoring Models
739(5)
Analytic Hierarchy Process
744(2)
Developing the Hierarchy
745(1)
Establishing Priorities Using AHP
746(8)
Pairwise Comparisons
746(2)
Pairwise Comparison Matrix
748(1)
Synthesization
749(1)
Consistency
750(2)
Other Pairwise Comparisons for the Car Selection Problem
752(2)
Using AHP to Develop an Overall Priority Ranking
754(14)
Summary
755(1)
Glossary
756(1)
Problems
756(9)
Case Problem: EZ Trailers, Inc.
765(1)
Appendix 17.1 Scoring Models with Excel
766(2)
Appendix A. Binomial Probabilities 768(7)
Appendix B. Poisson Probabilities 775(6)
Appendix C. Areas for the Standard Normal Distribution 781(1)
Appendix D. Values of e-λ 782(1)
Appendix E. References and Bibliography 783(2)
Appendix F. Self-Test Solutions and Answers to Even-Numbered Problems 785(30)
Index 815


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