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GLOBALEDITIONQuantitative Analysisfor ManagementTHIRTEENTH EDITIONBarry Render • Ralph M. Stair, Jr. • Michael E. Hanna • Trevor S. Hale

• Page 2 and 3: T H I R T E E N T H E D I T I O NG
• Page 4 and 5: About the AuthorsBarry Render is Pr
• Page 6 and 7: Brief ContentsCHAPTER 1 Introductio
• Page 8 and 9: CONTENTS 7CHAPTER 3 Decision Analys
• Page 10 and 11: CONTENTS 9Diet Problems 323Ingredie
• Page 12 and 13: CONTENTS 11APPENDIX D F Distributio
• Page 14 and 15: PrefaceOverviewWelcome to the thirt
• Page 16 and 17: PREFACE 15Chapter 5 Forecasting. A
• Page 18 and 19: PREFACE 17Instructor’s Solutions
• Page 20 and 21: CHAPTER1Introduction to Quantitativ
• Page 22 and 23: 1.3 The Quantitative Analysis Appro
• Page 24 and 25: 1.3 The Quantitative Analysis Appro
• Page 26 and 27: 1.4 How to Develop a Quantitative A
• Page 28 and 29: 1.5 THE ROLE OF COMPUTERS AND SPREA
• Page 30 and 31: 1.5 The Role of Computers and Sprea
• Page 32 and 33: 1.6 Possible Problems in the Quanti
• Page 34 and 35: 1.7 Implementation—Not Just the F
• Page 36 and 37: SELF-TEST 35Parameter A measurable
• Page 38 and 39: CASE STUDY 371-20 Farris Billiard S
• Page 40 and 41: CHAPTER2Probability Concepts and Ap
• Page 42 and 43: 2.1 Fundamental Concepts 41TABLE 2.
• Page 44 and 45: 2.1 Fundamental Concepts 43Unions a
• Page 46 and 47: 2.2 Revising Probabilities with Bay
• Page 48 and 49: 2.3 Further Probability Revisions 4
• Page 50 and 51: 2.4 Random Variables 49TABLE 2.5 Ex
• Page 52 and 53:

  2.5 Probability Distributions 51FIG

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  2.6 The Binomial Distribution 53FIG

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  2.6 The Binomial Distribution 55FIG

• Page 58 and 59:

  2.7 The Normal Distribution 57FIGUR

• Page 60 and 61:

  2.7 The Normal Distribution 59STEP

• Page 62 and 63:

  2.7 The Normal Distribution 61FIGUR

• Page 64 and 65:

  2.8 THE F DISTRIBUTION 63FIGURE 2.1

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  2.9 The Exponential Distribution 65

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  2.10 The Poisson Distribution 67whe

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  KEY EQUATIONS 69Normal Distribution

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  solved Problems 71Strongly agree 40

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  DISCUSSION QUESTIONS AND PROBLEMS 7

• Page 76 and 77:

  DISCUSSION QUESTIONS AND PROBLEMS 7

• Page 78 and 79:

  DISCUSSION QUESTIONS AND PROBLEMS 7

• Page 80 and 81:

  Appendix 2.1: Derivation of Bayes

• Page 82 and 83:

  CHAPTER3Decision AnalysisLEARNING O

• Page 84 and 85:

  3.3 DECISION MAKING UNDER UNCERTAIN

• Page 86 and 87:

  3.3 Decision Making Under Uncertain

• Page 88 and 89:

  3.4 Decision Making Under Risk 87IN

• Page 90 and 91:

  3.4 Decision Making Under Risk 89TA

• Page 92 and 93:

  3.4 Decision Making Under Risk 91FI

• Page 94 and 95:

  3.5 Using Software for Payoff Table

• Page 96 and 97:

  3.6 Decision Trees 95PROGRAM 3.2BKe

• Page 98 and 99:

  3.6 Decision Trees 97FIGURE 3.4Larg

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  3.6 Decision Trees 99EMV calculatio

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  3.7 How Probability Values Are Esti

• Page 104 and 105:

  3.7 How Probability Values Are Esti

• Page 106 and 107:

  3.8 Utility Theory 105FIGURE 3.7Sta

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  3.8 Utility Theory 107FIGURE 3.10Pr

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  GLOSSARY 109SummaryTherefore, alt

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  soLVED PROBLEMS 111ALTERNATIVEGOODM

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  soLVED PROBLEMS 113Solved Problem 3

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  From this information we can comput

• Page 118 and 119:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 120 and 121:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 122 and 123:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 124 and 125:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 126 and 127:

  CASE STUDY 125Case StudyStarting

• Page 128 and 129:

  CASE STUDY 127FIGURE 3.15Master

• Page 130 and 131:

  CHAPTER4Regression ModelsLEARNING O

• Page 132 and 133:

  4.2 Simple Linear Regression 1314.2

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  4.3 Measuring the Fit of the Regres

• Page 136 and 137:

  4.4 Assumptions of the Regression M

• Page 138 and 139:

  4.5 Testing the Model for Significa

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  4.5 Testing the Model for Significa

• Page 142 and 143:

  4.6 Using Computer Software for Reg

• Page 144 and 145:

  4.6 Using Computer Software for Reg

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  4.7 Multiple Regression Analysis 14

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  4.8 Binary or Dummy Variables 147Tw

• Page 150 and 151:

  4.10 Nonlinear Regression 149A vari

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  4.10 Nonlinear Regression 151FIGURE

• Page 154 and 155:

  153GLOSSARY A significant F valu

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  solved Problems 155Solved ProblemsS

• Page 158 and 159:

  DISCUSSION QUESTIONS AND PROBLEMS 1

• Page 160 and 161:

  DISCUSSION QUESTIONS AND PROBLEMS 1

• Page 162 and 163:

  DISCUSSION QUESTIONS AND PROBLEMS 1

• Page 164 and 165:

  Appendix 4.1: Formulas for Regressi

• Page 166 and 167:

  CHAPTER5ForecastingLEARNING OBJECTI

• Page 168 and 169:

  5.2 Components of a Time-Series 167

• Page 170 and 171:

  5.3 Measures of Forecast Accuracy 1

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  5.3 Measures of Forecast Accuracy 1

• Page 174 and 175:

  5.4 Forecasting Models—Random Var

• Page 176 and 177:

  5.4 Forecasting Models—Random Var

• Page 178 and 179:

  5.4 Forecasting Models—Random Var

• Page 180 and 181:

  5.5 Forecasting Models—Trend and

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  5.5 Forecasting Models—Trend and

• Page 184 and 185:

  5.6 Adjusting for Seasonal Variatio

• Page 186 and 187:

  5.7 Forecasting Models—Trend, Sea

• Page 188 and 189:

  5.7 Forecasting Models—Trend, Sea

• Page 190 and 191:

  5.7 Forecasting Models—Trend, Sea

• Page 192 and 193:

  5.8 Monitoring and Controlling Fore

• Page 194 and 195:

  KEY EQUATIONS 193Deseasonalized Dat

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  SELF-TEST 195andY n = predicted q

• Page 198 and 199:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 200 and 201:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 202 and 203:

  201 CASE STUDY Case StudyForecas

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  CHAPTER6Inventory Control ModelsLEA

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  6.2 Inventory Decisions 205Resource

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  6.3 Economic Order Quantity: Determ

• Page 210 and 211:

  6.3 Economic Order Quantity: Determ

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  6.3 Economic Order Quantity: Determ

• Page 214 and 215:

  6.4 Reorder Point: Determining When

• Page 216 and 217:

  6.5 EOQ Without the Instantaneous R

• Page 218 and 219:

  6.5 EOQ Without the Instantaneous R

• Page 220 and 221:

  6.6 Quantity Discount Models 219FIG

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  6.7 Use of Safety Stock 221PROGRAM

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  6.7 Use of Safety Stock 223How is t

• Page 226 and 227:

  6.7 Use of Safety Stock 225From App

• Page 228 and 229:

  6.8 Single-Period Inventory Models

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  6.8 Single-Period Inventory Models

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  6.8 Single-Period Inventory Models

• Page 234 and 235:

  6.10 Dependent Demand: The Case for

• Page 236 and 237:

  6.10 Dependent Demand: The Case for

• Page 238 and 239:

  6.11 Just-In-Time Inventory Control

• Page 240 and 241:

  GLOSSARY 239Summarynot only the fun

• Page 242 and 243:

  sOLVED PROBLEMS 241Solved ProblemsS

• Page 244 and 245:

  SELF-TEST 243Solved Problem 6-4The

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  DISCUSSION QUESTIONS AND PROBLEMS 2

• Page 248 and 249:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 250 and 251:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 252 and 253:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 254 and 255:

  Appendix 6.1: Inventory Control wit

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  CHAPTER7Linear Programming Models:G

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  7.2 Formulating LP Problems 257TABL

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  7.3 Graphical Solution to an LP Pro

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  7.3 Graphical Solution to an LP Pro

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  7.3 Graphical Solution to an LP Pro

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  7.3 Graphical Solution to an LP Pro

• Page 268 and 269:

  7.3 Graphical Solution to an LP Pro

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  7.4 SOLVING FLAIR FURNITURE’S LP

• Page 272 and 273:

  7.4 Solving Flair Furniture’s LP

• Page 274 and 275:

  7.4 Solving Flair Furniture’s LP

• Page 276 and 277:

  7.5 Solving Minimization Problems 2

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  7.5 Solving Minimization Problems 2

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  7.6 Four Special Cases in LP 279PRO

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  7.6 Four Special Cases in LP 281FIG

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  7.7 Sensitivity Analysis 283affect

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  7.7 Sensitivity Analysis 285PROGRAM

• Page 288 and 289:

  7.7 Sensitivity Analysis 287FIGURE

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  7.7 Sensitivity Analysis 289FIGURE

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  solved Problems 291Solved ProblemsS

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  solved Problems 293Solved Problem 7

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  SELF-TEST 295Self-Test●●●●

• Page 298 and 299:

  dISCUSSION QUESTIONS AND PROBLEMS 2

• Page 300 and 301:

  dISCUSSION QUESTIONS AND PROBLEMS 2

• Page 302 and 303:

  dISCUSSION QUESTIONS AND PROBLEMS 3

• Page 304 and 305:

  dISCUSSION QUESTIONS AND PROBLEMS 3

• Page 306 and 307:

  dISCUSSION QUESTIONS AND PROBLEMS 3

• Page 308 and 309:

  CHAPTER8Linear Programming Applicat

• Page 310 and 311:

  8.1 Marketing Applications 309PROGR

• Page 312 and 313:

  8.2 Manufacturing Applications 311P

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  8.2 Manufacturing Applications 313P

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  8.2 Manufacturing Applications 315I

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  8.3 Employee Scheduling Application

• Page 320 and 321:

  8.4 Financial Applications 319PROGR

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  8.4 Financial Applications 321PROGR

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  8.5 Ingredient Blending Application

• Page 326 and 327:

  8.5 Ingredient Blending Application

• Page 328 and 329:

  8.6 Other Linear Programming Applic

• Page 330 and 331:

  Problems 3294. What is the objectiv

• Page 332 and 333:

  Problems 331TYPE OF ADCOSTPER ADAUD

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  Problems 333DEVICEhours each week.

• Page 336 and 337:

  Problems 335(c) Are the laboratorie

• Page 338 and 339:

  CHAPTER9Transportation, Assignment,

• Page 340 and 341:

  9.1 The Transportation Problem 339w

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  9.1 The Transportation Problem 341L

• Page 344 and 345:

  9.2 The Assignment Problem 343PROGR

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  9.3 The Transshipment Problem 345PR

• Page 348 and 349:

  9.3 The Transshipment Problem 347Th

• Page 350 and 351:

  9.4 Maximal-Flow Problem 349FIGURE

• Page 352 and 353:

  9.5 Shortest-Route Problem 351start

• Page 354 and 355:

  9.6 Minimal-Spanning Tree Problem 3

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  SUMMARY 355IN ACTIONFacility Locati

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  Solved Problems 357The computer out

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  Discussion Questions and Problems 3

• Page 362 and 363:

  discussion Questions and Problems 3

• Page 364 and 365:

  discussion Questions and Problems 3

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  discussion Questions and Problems 3

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  discussion Questions and Problems 3

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  discussion Questions and Problems 3

• Page 372 and 373:

  Case Study 371TABLE 9.6Andrew-Carte

• Page 374 and 375:

  APPENDIX 9.1: USING QM FOR WINDOWS

• Page 376 and 377:

  CHAPTER10Integer Programming, Goal

• Page 378 and 379:

  10.1 Integer Programming 377FIGURE

• Page 380 and 381:

  10.1 Integer Programming 379PROGRAM

• Page 382 and 383:

  10.2 Modeling with 0-1 (Binary) Var

• Page 384 and 385:

  10.2 Modeling with 0-1 (Binary) Var

• Page 386 and 387:

  10.2 Modeling with 0-1 (Binary) Var

• Page 388 and 389:

  10.3 Goal Programming 387IN ACTIONC

• Page 390 and 391:

  10.3 Goal Programming 389A key idea

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  10.4 Nonlinear Programming 391Unlik

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  Glossary 393To solve this nonlinear

• Page 396 and 397:

  Solved Problems 395whereX 1 = numbe

• Page 398 and 399:

  Discussion Questions and Problems 3

• Page 400 and 401:

  Discussion Questions and Problems 3

• Page 402 and 403:

  Discussion Questions and Problems 4

• Page 404 and 405:

  BIBLIOGRAPHY 403Case StudyOakton Ri

• Page 406 and 407:

  CHAPTER11Project ManagementLEARNING

• Page 408 and 409:

  11.1 PERT/CPM 40711.1 PERT/CPMQuest

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  11.1 PERT/CPM 409FIGURE 11.1Network

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  11.1 PERT/CPM 4113. Latest start ti

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  11.1 PERT/CPM 413IN ACTIONProject M

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  11.1 PERT/CPM 415We know that the s

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  11.1 PERT/CPM 417wish to see a Gant

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  11.2 PERT/Cost 419FIGURE 11.9Gantt

• Page 422 and 423:

  11.2 PERT/Cost 421TABLE 11.7 Budget

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  11.3 Project Crashing 423IN ACTIONS

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  11.3 Project Crashing 425There are

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  11.3 Project Crashing 427For activi

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  KEY EQUATIONS 429Backward Pass A pr

• Page 432 and 433:

  SOLVED PROBLEMS 431Solved Problem 1

• Page 434 and 435:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 436 and 437:

  435 DISCUSSION QUESTIONS AND PRO

• Page 438 and 439:

  437 DISCUSSION QUESTIONS AND PRO

• Page 440 and 441:

  439 DISCUSSION QUESTIONS AND PRO

• Page 442 and 443:

  441 CASE STUDY Case StudyFamily

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  443 APPENDIX 11.1: PROJECT MANAG

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  CHAPTER12Waiting Lines and Queuing

• Page 448 and 449:

  12.2 Characteristics of a Queuing S

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  12.2 Characteristics of a Queuing S

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  12.2 Characteristics of a Queuing S

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  12.3 SINGLE-CHANNEL QUEUING MODEL W

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  12.3 SINGLE-CHANNEL QUEUING MODEL W

• Page 458 and 459:

  12.4 MULTICHANNEL QUEUING MODEL WIT

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  12.4 MULTICHANNEL QUEUING MODEL WIT

• Page 462 and 463:

  12.6 FINITE POPULATION MODEL (M/M/1

• Page 464 and 465:

  12.7 Some General Operating Charact

• Page 466 and 467:

  KEY EQUATIONS 465GlossaryBalking Th

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  solved Problems 467Solved ProblemsS

• Page 470 and 471:

  Self-Test 469Solved Problem 12-4Vac

• Page 472 and 473:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 474 and 475:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 476 and 477:

  CASE STUDY 475with an average of

• Page 478 and 479:

  BIBLIOGRAPHY 477Case StudyWinter

• Page 480 and 481:

  CHAPTER13Simulation ModelingLEARNIN

• Page 482 and 483:

  13.2 Monte Carlo Simulation 481HIST

• Page 484 and 485:

  13.2 Monte Carlo Simulation 483Prob

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  13.2 Monte Carlo Simulation 485TABL

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  13.2 Monte Carlo Simulation 487PROG

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  13.3 Simulation and Inventory Analy

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  13.3 Simulation and Inventory Analy

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  13.3 Simulation and Inventory Analy

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  13.4 Simulation of a Queuing Proble

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  13.5 Simulation Model for a Mainten

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  13.5 Simulation Model for a Mainten

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  13.5 Simulation Model for a Mainten

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  13.6 Other Simulation Issues 503Eco

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  SOLVED PROBLEMS 505Solved ProblemsS

• Page 508 and 509:

  SELF-TEST 507(1)CUSTOMERNUMBER(2)RA

• Page 510 and 511:

  509diSCUSSION QUESTIONS AND PROB

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  511diSCUSSION QUESTIONS AND PROB

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  513 DISCUSSION QUESTIONS AND PRO

• Page 516 and 517:

  515 CASE STUDY TABLE 13.15 Incom

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  517 CASE STUDY FIGURE 13.6Temper

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  CHAPTER14Markov AnalysisLEARNING OB

• Page 522 and 523:

  14.1 States and State Probabilities

• Page 524 and 525:

  14.3 Predicting Future Market Share

• Page 526 and 527:

  14.5 Equilibrium Conditions 52514.5

• Page 528 and 529:

  14.5 Equilibrium Conditions 527MODE

• Page 530 and 531:

  14.6 Absorbing States and the Funda

• Page 532 and 533:

  14.6 Absorbing States and the Funda

• Page 534 and 535:

  solvED PROBLEMS 533Formula for c

• Page 536 and 537:

  solvED PROBLEMS 535Solved Problem 1

• Page 538 and 539:

  537 DISCUSSION QUESTIONS AND PRO

• Page 540 and 541:

  539disCUSSION QUESTIONS AND PROB

• Page 542 and 543:

  541 CASE STUDY 14-31 The followi

• Page 544 and 545:

  543 APPENDIX 14.1: MARKOV ANALYS

• Page 546 and 547:

  APPENDIX 14.2: MARKOV ANALYSIS WITH

• Page 548 and 549:

  CHAPTER15Statistical Quality Contro

• Page 550 and 551:

  15.2 Statistical Process Control 54

• Page 552 and 553:

  15.3 Control Charts for Variables 5

• Page 554 and 555:

  15.3 Control Charts for Variables 5

• Page 556 and 557:

  15.4 Control Charts for Attributes

• Page 558 and 559:

  15.4 CONTROL CHARTS FOR ATTRIBUTES

• Page 560 and 561:

  KEY EQUATIONS 559PROGRAM 15.4Ex

• Page 562 and 563:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 564 and 565:

  DISCUSSION QUESTIONS AND PROBLEMS

• Page 566 and 567:

  Appendix 15.1: Using QM for Windows

• Page 568 and 569:

  AppendicesA. Areas Under the Standa

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  Appendix A: Areas Under the Standar

• Page 572 and 573:

  Appendix B: Binomial Probabilities

• Page 574 and 575:

  Appendix B: Binomial Probabilities

• Page 576 and 577:

  APPENDIX C: VALUES OF e -l FOR USE

• Page 578 and 579:

  APPENDIX D: F DISTRIBUTION VALUES 5

• Page 580 and 581:

  APPENDIX E: USING POM-QM FOR WINDOW

• Page 582 and 583:

  APPENDIX F: USING EXCEL QM AND EXCE

• Page 584 and 585:

  Appendix G: Solutions to Selected P

• Page 586 and 587:

  Appendix G: Solutions to Selected P

• Page 588 and 589:

  Appendix H: Solutions to Self-Tests

• Page 590 and 591:

  Index0-1 (binary) variables, 381-38

• Page 592 and 593:

  INDEX 591finding exponential probab

• Page 594 and 595:

  INDEX 593Market shares, 375, 519, 5

• Page 596 and 597:

  INDEX 595Random (R) component, of t

• Page 598 and 599:

  INDEX 597Vertical axis, 130VLOOKUP

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  GLOBALEDITIONFor these Global Editi