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Quantitative Analysis for Management,9780131857025

Quantitative Analysis for Management

by ; ;
Edition:
10th
ISBN13:

9780131857025

ISBN10:
0131857029
Format:
Hardcover w/CD
Pub. Date:
1/1/2009
Publisher(s):
Prentice Hall
List Price: $226.65

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Summary

Easy to understand-even for learners with limited math backgrounds, this book uses a modeling approach to provide thorough coverage of the basic techniques in quantitative methods and focuses on the managerial applications of these techniques. An interesting and reader friendly writing style makes for a clear presentation, complete with all the necessary assumptions and mathematical details.Chapter topics include probability concepts and applications, decision models and decision trees, regression models, forecasting, inventory control models, linear programming modeling applications and computer analyses, network models, project management, simulation modeling, and more.For an introduction toquantitative analysis, quantitative management, operations research, or management science-especially for those individuals preparing for work in agricultural economics and health care fields.

Author Biography

Michael E. Hanna is Professor of Decision Sciences at the University of Houston - Clear Lake (UHCL). Ralph Stair is a retired professor in the College of Business at Florida State University. Barry Render is the Charles Harwood Distinguished Professor of Management Science at the Roy E. Crummer Graduate School of Business at Rollins College in Winter Park, Florida.

Table of Contents

Preface xv
Introduction to Quantitative Analysis
1(20)
Introduction
2(1)
What Is Quantitative Analysis?
2(1)
The Quantitative Analysis Approach
3(4)
Defining the Problem
3(1)
Developing a Model
3(1)
Acquiring Input Data
4(1)
Developing a Solution
5(1)
Testing the Solution
5(1)
Analyzing the Results and Sensitivity Analysis
5(2)
Implementing the Results
7(1)
The Quantitative Analysis Approach and Modeling in the Real World
7(1)
How to Develop a Quantitative Analysis Model
7(2)
The Advantages of Mathematical Modeling
9(1)
Mathematical Models Categorized by Risk
9(1)
The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
9(3)
Possible Problems in the Quantitative Analysis Approach
12(4)
Defining the Problem
12(2)
Developing a Model
14(1)
Acquiring Input Data
14(1)
Developing a Solution
15(1)
Testing the Solution
15(1)
Analyzing the Results
15(1)
Implementation---Not Just the Final Step
16(5)
Lack of Commitment and Resistance to Change
16(1)
Lack of Commitment by Quantitative Analysts
16(1)
Summary
17(1)
Glossary
17(1)
Key Equations
17(1)
Self-Test
18(1)
Discussion Questions and Problems
18(1)
Case Study: Food and Beverages at Southwestern University Football Games
19(1)
Bibliography
20(1)
Probability Concepts and Applications
21(46)
Introduction
22(1)
Fundamental Concepts
22(2)
Types of Probability
23(1)
Mutually Exclusive and Collectively Exhaustive Events
24(3)
Adding Mutually Exclusive Events
26(1)
Law of Addition for Events That Are Not Mutually Exclusive
26(1)
Statistically Independent Events
27(1)
Statistically Dependent Events
28(2)
Revising Probabilities with Bayes' Theorem
30(2)
General Form of Bayes' Theorem
32(1)
Further Probability Revisions
32(2)
Random Variables
34(1)
Probability Distributions
35(4)
Probability Distribution of a Discrete Random Variable
35(1)
Expected Value of a Discrete Probability Distribution
36(1)
Variance of a Discrete Probability Distribution
37(1)
Probability Distribution of a Continuous Random Variable
38(1)
The Binomial Distribution
39(3)
Solving Problems with the Binomial Formula
40(1)
Solving Problems with Binomial Tables
40(2)
The Normal Distribution
42(7)
Area Under the Normal Curve
42(2)
Using the Standard Normal Table
44(3)
Haynes Construction Company Example
47(2)
The Exponential Distribution
49(1)
The Poisson Distribution
49(18)
Summary
50(1)
Glossary
51(1)
Key Equations
51(1)
Solved Problems
52(4)
Self-Test
56(1)
Discussion Questions and Problems
57(4)
Internet Homework Problems
61(1)
Case Study: Century Chemical Company
61(1)
Case Study: WTVX
62(1)
Bibliography
62(1)
Appendix 2.1 Derivation of Bayes' Theorem
62(1)
Appendix 2.2 Basic Statistics Using Excel
63(4)
Decision Analysis
67(48)
Introduction
68(1)
The Six Steps in Decision Making
68(2)
Types of Decision-Making Environments
70(1)
Decision-Making Under Uncertainty
71(4)
Maximax
71(1)
Maximin
72(1)
Criterion of Realism (Hurwicz Criterion)
72(2)
Equally Likely (Laplace)
74(1)
Minimax Regret
74(1)
Decision Making Under Risk
75(6)
Expected Monetary Value
75(1)
Expected Value of Perfect Information
76(1)
Expected Opportunity Loss
77(1)
Sensitivity Analysis
78(1)
Using Excel QM to Solve Decision Theory Problems
79(2)
Decision Trees
81(6)
Sensitivity Analysis
86(1)
How Probability Values Are Estimated by Bayesian Analysis
87(3)
Calculating Revised Probabilities
87(2)
Potential Problem in Using Survey Results
89(1)
Utility Theory
90(25)
Measuring Utility and Constructing a Utility Curve
90(3)
Utility as a Decision-Making Criterion
93(3)
Summary
96(1)
Glossary
96(1)
Key Equations
97(1)
Solved Problems
97(6)
Self-Test
103(1)
Discussion Questions and Problems
104(6)
Internet Homework Problems
110(1)
Case Study: Starting Right Corporation
110(1)
Case Study: Blake Electronics
110(2)
Internet Case Studies
112(1)
Bibliography
112(1)
Appendix 3.1 Decision Models with QM for Windows
113(1)
Appendix 3.2 Decision Trees with QM for Windows
114(1)
Appendix 3.3 Using Excel for Bayes' Theorem
114(1)
Regression Models
115(34)
Introduction
116(1)
Scatter Diagrams
116(1)
Simple Linear Regression
117(2)
Measuring the Fit of the Regression Model
119(3)
Coefficient of Dertermination
121(1)
Correlation Coefficient
121(1)
Using Computer Software for Regression
122(2)
Assumptions of the Regression Model
124(2)
Estimating the Variance
125(1)
Testing the Model for Significance
126(1)
The Analysis of Variance Table
127(1)
Multiple Regression Analysis
127(3)
Binary or Dummy Variables
130(1)
Model Building
130(2)
Nonlinear Regression
132(3)
Cautions and Pitfalls in Regression Analysis
135(14)
Summary
136(1)
Glossary
136(1)
Key Equations
136(1)
Solved Problems
137(2)
Self-Test
139(1)
Discussion Questions and Problems
140(3)
Case Study: North-South Airline
143(1)
Bibliography
144(1)
Appendix 4.1 Formulas for Regression Calculations
144(2)
Appendix 4.2 Regression Models Using QM for Windows
146(3)
Forecasting
149(40)
Introduction
150(1)
Types of Forecasts
150(2)
Time-Series Models
150(1)
Causal Models
151(1)
Qualitative Models
151(1)
Scatter Diagrams and Time Series
152(2)
Measures of Forecast Accuracy
154(2)
Time-Series Forecasting Models
156(19)
Decomposition of a Time Series
156(1)
Moving Averages
157(3)
Exponential Smoothing
160(4)
Trend Projections
164(2)
Seasonal Variations
166(2)
Seasonal Variations with Trend
168(2)
The Decomposition Method of Forecasting with Trend and Seasonal Components
170(4)
Using Regression with Trend and Seasonal Components
174(1)
Monitoring and Controlling Forecasts
175(2)
Adaptive Smoothing
177(1)
Using the Computer to Forecast
177(12)
Summary
178(1)
Glossary
179(1)
Key Equations
179(1)
Solved Problems
180(2)
Self-Test
182(1)
Discussion Questions and Problems
183(2)
Internet Homework Problems
185(1)
Case Study: Forecasting Attendance at SWU Football Games
185(1)
Internet Case Study
186(1)
Bibliography
186(1)
Appendix 5.1 Forecasting with QM for Windows
187(2)
Inventory Control Models
189(52)
Introduction
190(1)
Importance of Inventory Control
191(1)
Decoupling Function
191(1)
Storing Resources
191(1)
Irregular Supply and Demand
191(1)
Quantity Discounts
191(1)
Avoiding Stockouts and Shortages
192(1)
Inventory Decisions
192(1)
Economic Order Quantity: Determining How Much to Order
193(7)
Inventory Costs in the EOQ Situation
195(1)
Finding the EOQ
196(1)
Sumco Pump Company Example
197(1)
Purchase Cost of Inventory Items
198(1)
Sensitivity Analysis with the EOQ Model
199(1)
Reorder Point: Determining When to Order
200(1)
EOQ Without the Instantaneous Receipt Assumption
201(5)
Annual Carrying Cost for Production Run Model
202(1)
Annual Setup Cost of Annual Ordering Cost
202(1)
Determining the Optimal Production Quantity
203(1)
Brown Manufacturing
203(3)
Quantity Discount Models
206(4)
Use of Safety Stock
210(6)
ROP with Known Stockout Costs
211(3)
Safety Stock with Unknown Stockout Costs
214(2)
ABC Analysis
216(2)
Dependent Demand: The Case for Material Requirements Planning
218(6)
Material Structure Tree
218(1)
Gross and Net Material Requirements Plan
219(2)
Two or More End Products
221(3)
Just-in-Time Inventory Control
224(1)
Enterprise Resource Planning
225(16)
Summary
226(1)
Glossary
226(1)
Key Equations
226(1)
Solved Problems
227(2)
Self-Test
229(1)
Discussion Questions and Problems
230(6)
Internet Homework Problems
236(1)
Case Study: Sturdivant Sound Systems
236(1)
Case Study: Martin--Pullin Bicycle Corporation
237(1)
Internet Case Studies
237(1)
Bibliography
237(1)
Appendix 6.1 Inventory Control with QM for Windows
238(3)
Linear Programming Models: Graphical and Computer Methods
241(52)
Introduction
242(1)
Requirements of a Linear Programming Problem
242(2)
Basic Assumptions of LP
243(1)
Formulating LP Problems
244(2)
Flair Furniture Company
244(2)
Graphical Solution to a LP Problem
246(10)
Graphical Representation of Constraints
246(5)
Isoprofit Line Solution Method
251(3)
Corner Point Solution Method
254(2)
Solving Flair Furniture's LP Problem Using QM for Windows and Excel
256(5)
Using QM for Windows
256(1)
Using Excel's Solver Command to Solve LP Problems
257(4)
Solving Minimization Problems
261(4)
Holiday Meal Turkey Ranch
261(4)
Four Special Cases in LP
265(4)
No Feasible Solution
265(2)
Unboundedness
267(1)
Redundancy
267(1)
Alternate Optimal Solutions
268(1)
Sensitivity Analysis
269(24)
High Note Sound Company
270(1)
Changes in the Objective Function Coefficient
271(1)
QM for Windows and Changes in Objective Function Coefficients
272(1)
Excel Solver and Changes in Objective Function Coefficients
272(2)
Changes in the Technological Coefficients
274(1)
Changes in the Resources or Right-Hand-Side Values
275(2)
QM For Windows and Changes in Right-Hand-Side Values
277(1)
Excel Solver and Changes in Right-Hand-Side Values
277(1)
Summary
277(1)
Glossary
277(1)
Solved Problems
278(4)
Self-Test
282(1)
Discussion Questions and Problems
283(7)
Internet Homework Problems
290(1)
Case Study: Mexicana Wire Works
290(2)
Internet Case Study
292(1)
Bibliography
292(1)
Linear Programming Modeling Applications: With Computer Analyses in Excel and QM for Windows
293(40)
Introduction
294(1)
Marketing Applications
294(4)
Media Selection
294(2)
Marketing Research
296(2)
Manufacturing Applications
298(5)
Production Mix
298(1)
Production Scheduling
299(4)
Employee Scheduling Applications
303(4)
Assignment Problems
303(2)
Labor Planning
305(2)
Financial Applications
307(1)
Portfolio Selection
307(1)
Transportation Applications
308(5)
Shipping Problem
308(1)
Truck Loading Problem
309(4)
Transshipment Applications
313(2)
Distribution Centers
313(2)
Ingredient Blending Applications
315(18)
Diet Problems
316(1)
Ingredient Mix and Blending Problems
316(3)
Summary
319(1)
Self-Test
320(1)
Problems
320(8)
Internet Homework Problems
328(1)
Case Study: Red Brand Canners
328(2)
Case Study: Chase Manhattan Bank
330(1)
Bibliography
331(2)
Linear Programming: The Simplex Method
333(60)
Introduction
334(1)
How to Set Up the Initial Simplex Solution
334(6)
Converting the Constraints to Equations
335(1)
Finding an Initial Solution Algebraically
335(1)
The First Simplex Tableau
336(4)
Simplex Solution Procedures
340(1)
The Second Simplex Tableau
341(4)
Interpreting the Second Tableau
344(1)
Developing the Third Tableau
345(3)
Review of Procedures for Solving LP Maximization Problems
348(1)
Surplus and Artificial Variables
348(2)
Surplus Variables
349(1)
Artificial Variables
349(1)
Surplus and Artificial Variables in the Objective Function
350(1)
Solving Minimization Problems
350(9)
Graphical Analysis
351(1)
Converting the Constraints and Objective Function
352(1)
Rules of the Simplex Method for Minimization Problems
353(1)
First Simplex Tableau for the Muddy River Chemical Corporation Problem
353(2)
Developing a Second Tableau
355(1)
Developing a Third Tableau
356(2)
Fourth Tableau for the Muddy River Chemical Corporation Problem
358(1)
Review of Procedures for Solving LP Minimization Problems
359(1)
Special Cases
360(2)
Infeasibility
360(1)
Unbounded Solutions
360(1)
Degeneracy
361(1)
More Than One Optimal Solution
362(1)
Sensitivity Analysis with the Simplex Tableau
362(6)
High Note Sound Company Revisited
362(1)
Changes in the Objective Function Coefficients
363(2)
Changes in Resources or RHS Values
365(2)
Sensitivity Analysis by Computer
367(1)
The Dual
368(4)
Dual Formulation Procedures
370(1)
Solving the Dual of the High Note Sound Company Problem
370(2)
Karmarkar's Algorithm
372(21)
Summary
372(1)
Glossary
372(1)
Key Equation
373(1)
Solved Problems
373(7)
Self-Test
380(1)
Discussion Questions and Problems
381(8)
Internet Homework Problems
389(1)
Case Study: Coastal States Chemicals and Fertilizers
390(1)
Bibliography
391(2)
Transportation and Assignment Models
393(58)
Introduction
394(1)
Transportation Model
394(1)
Assignment Model
394(1)
Special-Purpose Algorithms
394(1)
Setting Up a Transportation Problem
395(1)
Developing an Initial Solution: Northwest Corner Rule
396(2)
Stepping-Stone Method: Finding a Least-Cost Solution
398(9)
Testing the Solution for Possible Improvement
399(3)
Obtaining an Improved Solution
402(5)
Modi Method
407(3)
How to Use the Modi Approach
407(1)
Solving the Executive Furniture Corporation Problem with Modi
408(2)
Vogel's Approximation Method: Another Way to Find an Initial Solution
410(3)
Unbalanced Transportation Problems
413(2)
Demand Less Than Supply
413(1)
Demand Greater Than Supply
413(2)
Degeneracy in Transportation Problems
415(2)
Degeneracy in an Initial Solution
415(1)
Degeneracy During Later Solution Stages
416(1)
More Than One Optimal Solution
417(1)
Maximization Transportation Problems
417(1)
Unacceptable or Prohibited Routes
417(1)
Facility Location Analysis
418(3)
Locating a New Factory for Hardgrave Machine Company
418(3)
Approach of the Assignment Model
421(7)
The Hungarian Method (Flood's Technique)
422(4)
Making the Final Assignment
426(2)
Unbalanced Assignment Problems
428(1)
Maximization Assignment Problems
428(23)
Summary
430(1)
Glossary
430(1)
Key Equations
431(1)
Solved Problems
431(7)
Self-Test
438(1)
Discussion Questions and Problems
438(9)
Internet Homework Problems
447(1)
Case Study: Andrew--Carter, Inc.
447(1)
Case Study: Old Oregon Wood Store
448(1)
Internet Case Studies
449(1)
Bibliography
449(1)
Appendix 10.1 Using QM for Windows
449(1)
Appendix 10.2 Comparison of Simplex Algorithm and Transportation Algorithm
450(1)
Integer Programming, Goal Programming, and Nonlinear Programming
451(46)
Introduction
452(1)
Integer Programming
452(11)
Harrison Electric Company Example of Integer Programming
453(1)
Branch and Bound Method
454(1)
Harrison Electric Company Revisited
455(3)
Using Software to Solve the Harrison Integer Programming Problem
458(2)
Mixed Integer Programming Problem Example
460(3)
Modeling with 0--1 (Binary) Variables
463(5)
Capital Budgeting Example
463(1)
Limiting the Number of Alternatives Selected
464(1)
Dependent Selections
464(1)
Fixed Charge Problem Example
464(2)
Financial Investment Example
466(2)
Goal Programming
468(11)
Example of Goal Programming: Harrison Electric Company Revisited
469(1)
Extension to Equally Important Multiple Goals
470(1)
Ranking Goals with Priority Levels
471(1)
Solving Goal Programming Problems Graphically
472(2)
Modified Simplex Method for Goal Programming
474(3)
Goal Programming with Weighted Goals
477(2)
Nonlinear Programming
479(18)
Nonlinear Objective Function and Linear Constraints
479(1)
Both Nonlinear Objective Function and Nonlinear Constraints
480(2)
Linear Objective Function with Nonlinear Constraints
482(1)
Computational Procedures for Nonlinear Programming
482(1)
Summary
483(1)
Glossary
483(1)
Solved Problems
484(2)
Self-Test
486(1)
Discussion Questions and Problems
487(5)
Internet Homework Problems
492(1)
Case Study: Schank Marketing Research
492(1)
Case Study: Oakton River Bridge
492(1)
Case Study: Puyallup Mall
493(1)
Bibliography
494(3)
Network Models
497(28)
Introduction
498(1)
Minimal-Spanning Tree Technique
498(3)
Maximal-Flow Technique
501(4)
Shortest-Route Technique
505(20)
Summary
509(1)
Glossary
509(1)
Solved Problems
509(3)
Self-Test
512(1)
Discussion Questions and Problems
513(6)
Internet Homework Problems
519(1)
Case Study: Binder's Beverage
519(1)
Case Study: Southwestern University Traffic Problems
520(1)
Internet Case Study
521(1)
Bibliography
521(1)
Appendix 12.1 Network Models with QM for Windows
521(4)
Project Management
525(42)
Introduction
526(1)
Framework of Pert and CPM
526(1)
Pert
527(12)
General Foundry Example of Pert
527(2)
Drawing the Pert Network
529(1)
Activity Times
530(1)
How to Find the Critical Path
531(5)
Probability of Project Completion
536(2)
What Pert Was Able to Provide
538(1)
Sensitivity Analysis and Project Management
538(1)
Pert/Cost
539(6)
Planning and Scheduling Project Costs: Budgeting Process
540(3)
Monitoring and Controlling Project Costs
543(2)
Critical Path Method
545(5)
Project Crashing with CPM
545(2)
Project Crashing with Linear Programming
547(3)
Other Topics in Project Management
550(17)
Subprojects
551(1)
Milestones
551(1)
Resource Leveling
551(1)
Software
551(1)
Summary
551(1)
Glossary
552(1)
Key Equations
552(1)
Solved Problems
553(2)
Self-Test
555(1)
Discussion Questions and Problems
556(4)
Internet Homework Problems
560(1)
Case Study: Southwestern University Stadium Construction
561(1)
Case Study: Family Planning Research Center of Nigeria
562(1)
Internet Case Studies
563(1)
Bibliography
563(1)
Appendix 13.1 Project Management with QM for Windows
564(3)
Waiting Lines and Queuing Theory Models
567(40)
Introduction
568(1)
Waiting Line Costs
568(2)
Characteristics of a Queuing System
570(6)
Arrival Characteristics
570(2)
Waiting Line Characteristics
572(1)
Service Facility Characteristics
572(2)
Identifying Models Using Kendall Notation
574(2)
Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/I)
576(6)
Assumptions of the Model
576(1)
Queuing Equations
577(1)
Arnold's Muffler Shop Case
578(4)
Enhancing the Queuing Environment
582(1)
Multiple-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m)
582(5)
Equations for the Multichannel Queuing Model
583(1)
Arnold's Muffler Shop Revisited
584(3)
Constant Service Time Model (M/D/I)
587(2)
Equations for the Constant Service Time Model
587(1)
Garcia-Golding Recycling, Inc.
588(1)
Finite Population Model (M/M/I with Finite Source)
589(3)
Equations for the Finite Population Model
589(1)
Department of Commerce Example
590(2)
Some General Operating Characteristic Relationships
592(1)
More Complex Queuing Models and the Use of Simulation
592(15)
Summary
593(1)
Glossary
593(1)
Key Equations
594(1)
Solved Problems
595(3)
Self-Test
598(1)
Discussion Questions and Problems
599(3)
Internet Homework Problems
602(1)
Case Study: New England Foundry
602(2)
Case Study: Winter Park Hotel
604(1)
Internet Case Study
604(1)
Bibliography
604(1)
Appendix 14.1 Using QM for Windows
605(2)
Simulation Modeling
607(44)
Introduction
608(1)
Advantages and Disadvantages of Simulation
609(1)
Monte Carlo Simulation
610(9)
Using QM for Windows for Simulation
616(1)
Simulation with Excel Spreadsheets
617(2)
Simulation and Inventory Analysis
619(6)
Simkin's Hardware Store
619(4)
Analyzing Simkin's Inventory Costs
623(2)
Simulation of a Queuing Problem
625(3)
Port of New Orleans
625(2)
Using Excel to Simulate the Port of New Orleans Queuing Problem
627(1)
Fixed Time Increment and Next Event Increment Simulation Models
628(1)
Simulation Model for a Maintenance Policy
628(6)
Three Hills Power Company
629(4)
Cost Analysis of the Simulation
633(1)
Building an Excel Simulation Model for Three Hills Power Company
634(1)
Two Other Types of Simulation Models
634(2)
Operational Gaming
634(2)
Systems Simulation
636(1)
Verification and Validation
636(1)
Role of Computers in Simulation
637(14)
Summary
638(1)
Glossary
638(1)
Solved Problems
638(4)
Self-Test
642(1)
Discussion Questions and Problems
643(5)
Internet Homework Problems
648(1)
Case Study: Alabama Airlines
648(1)
Case Study: Statewide Development Corporation
649(1)
Internet Case Studies
650(1)
Bibliography
650(1)
Markov Analysis
651(30)
Introduction
652(1)
States and State Probabilities
652(3)
The Vector of State Probabilities for Three Grocery Stores Example
653(2)
Matrix of Transition Probabilities
655(1)
Transition Probabilities for the Three Grocery Stores
655(1)
Predicting Future Market Shares
656(1)
Markov Analysis of Machine Operations
657(1)
Equilibrium Conditions
658(3)
Absorbing States and the Fundamental Matrix: Accounts Receivable Application
661(20)
Summary
666(1)
Glossary
666(1)
Key Equations
666(1)
Solved Problems
667(4)
Self-Test
671(1)
Discussion Questions and Problems
671(4)
Internet Homework Problems
675(1)
Case Study: Rentall Trucks
675(1)
Internet Case Studies
676(1)
Bibliography
677(1)
Appendix 16.1 Markov Analysis with QM for Windows
677(1)
Appendix 16.2 Markov Analysis with Excel
678(3)
Statistical Quality Control
681(22)
Introduction
682(1)
Defining Quality and TQM
682(1)
Statistical Process Control
683(2)
Variability in the Process
683(2)
Control Charts for Variables
685(5)
The Central Limit Theorem
685(1)
Setting x-Chart Limits
686(2)
Setting Range Chart Limits
688(2)
Conrol Charts for Attributes
690(13)
p-Charts
690(3)
c-Charts
693(1)
Summary
694(1)
Glossary
694(1)
Key Equations
694(1)
Solved Problems
695(2)
Self-Test
697(1)
Discussion Questions and Problems
697(2)
Internet Homework Problems
699(1)
Case Study: Morristown Daily Tribune
700(1)
Internet Case Study
700(1)
Bibliography
701(1)
Appendix 17.1 Using QM for Windows for SPC
701(2)
APPENDICES
703(22)
Appendix A Areas Under the Standard Normal Curve
704(2)
Appendix B Binomial Probabilities
706(5)
Appendix C Values of eλ for Use in the Poisson Distribution
711(1)
Appendix D Using QM for Windows
712(4)
Appendix E Using Excel QM
716(1)
Appendix F Solutions to Selected Problems
717(4)
Appendix G Solutions to Self-Tests
721(4)
Index 725


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