简介
Summary:
Publisher Summary 1
This text/CD-ROM package provides students with data analysis and modeling tools that will enhance their ability to use data to understand customers and markets and to improve their products and services. It describes the modeling process, lists its steps, and illustrates these steps both within each chapter and across the chapters of the book. Detailed modeling examples go through the entire process of identifying an opportunity or problem, formulating an appropriate model, and discussing issues related to data collection. Pedagogical features include exercises, discussion questions, and cases. The CD-ROMs offer evaluation versions of Crystal Ball 2000 Professional Edition and Microsoft Project 2000. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 2
Rather than giving instruction in models and solving problems, this textbook focuses on the process of modeling and the use of models in analyzing various managerial situations. The process of modeling is highly relevant to all business disciplines and is a critical skill for all professionals. The emphasis of this text will be on the integration and development of modeling skills including problem recognition, data collection, model formulation, analysis, and communicating and implementing the results.
目录
Preface p. xvii
About the Authors p. xxiii
Decision Making and Quantitative Modeling p. 1
Quantitative Business Modeling p. 7
Definition of a Model p. 9
Benefits and Drawbacks of Modeling p. 10
Types of Models p. 11
Effective Modelers p. 14
The Modeling Process p. 14
A Five-Step Modeling Process p. 16
Opportunity/Problem Recognition p. 17
Model Formulation p. 17
Data Collection p. 21
Analysis of the Model p. 23
Implementation and Project Management p. 25
Detailed Modeling Example p. 28
Opportunity/Problem Recognition p. 28
Model Formulation p. 29
Data Collection p. 30
Analysis of the Model p. 30
Implementation and Project Management p. 30
Software for Modeling p. 33
Questions p. 33
Experiential Exercises p. 34
Modeling Exercises p. 35
Case: Henry Ford Hospital p. 36
Endnotes p. 37
Bibliography p. 37
Data Collection and Analysis p. 38
Data Collection p. 39
Summarizing Data p. 42
Descriptive Statistics p. 42
Statistical Displays p. 44
Probability and Random Variables p. 47
Subjective Probablility p. 48
Logical Probability p. 48
Experimental Probability p. 48
Event Relationships and Probability Laws p. 48
Probability Distributions p. 51
Common Probability Distributions p. 52
The Binomial Distribution p. 53
The Poisson Distribution p. 54
The Exponential Distribution p. 55
The Normal Distribution p. 56
The t Distribution p. 58
Distributions of Sample Statistics p. 58
Chi-Square Goodness of Fit Test p. 60
Point and Interval Estimation p. 64
Interval Estimation of a Mean p. 65
Determining the Size of the Sample for a Normal Distribution p. 68
Interval Estimation and Determination of Sample Size for a Proportion p. 69
Hypothesis Testing p. 71
Hypothesis Tests for Means p. 73
Comparing Multiple Means--Analysis of Variance (ANOVA) p. 77
Detailed Modeling Example p. 81
Opportunity/Problem Recognition p. 81
Model Formulation p. 82
Data Collection p. 82
Analysis of the Model p. 85
Implementation p. 86
Questions p. 89
Experiential Exercise p. 89
Modeling Exercises p. 90
Case: Fiberease Inc. p. 93
Case: InterAccess Inc. p. 95
Case: eApp Inc. p. 95
Endnote p. 96
Bibliography p. 96
Statistical Models: Regression and Forecasting p. 97
The Modeling Process for Statistical Studies p. 99
The Simple Linear Regression Model p. 100
Calculating the Regression Model Parameters p. 103
The Coefficient of Determination and the Correlation Coefficient p. 105
Regression Analysis Assumptions p. 109
Using the Regression Model p. 110
The Multiple Regression Model p. 112
Developing Regression Models p. 115
Identify Candidate Independent Variables to Include in the Model p. 115
Transform the Data p. 117
Select the Variables to Include in the Model p. 118
Analyze the Residuals p. 118
Regression Hypothesis Tests p. 119
Time Series Analysis p. 121
Components of a Time Series p. 121
Time Series Models p. 123
Detailed Modeling Example p. 130
Opportunity/Problem Recognition p. 130
Model Formulation p. 131
Data Collection p. 131
Analysis of the Model p. 131
Implementation p. 135
Questions p. 140
Experiential Exercise p. 140
Modeling Exercises p. 141
Case: Resale Value of Long's Automobile p. 144
Case: Lewisville Crate Company p. 144
Bibliography p. 147
Optimization and Mathematical Programming p. 148
The Modeling Process for Optimization Studies p. 153
Optimization p. 153
The Modeling Process p. 154
Structure of the Chapter p. 156
Linear Programming p. 156
The Output-Mix Problem p. 157
The Blending Problem p. 157
Formulating the Linear Programming Model p. 157
Output-Mix and Blending Problems: Two Examples p. 158
Example: The Blending (Minimization) Problem p. 160
The General LP Model p. 161
Advantages, Assumptions, and Solution Methods p. 162
Distribution Problems; Transportation, Transshipment, Assignment p. 164
Analysis of the Model by the Graphical Method p. 165
A Maximization Problem p. 165
A Minimization Problem p. 172
Utilization of the Resources--Slack and Surplus Variables p. 174
Special Situations p. 175
Solving Linear Programming Models with Excel p. 177
Using Excel's Solver p. 177
Solving Large Problems p. 181
Back to Startron's Dilemma p. 185
Sensitivity ("What-If") Analysis p. 189
Why a Sensitivity Analysis? p. 189
Sensitivity Analysis: Objective Function p. 190
Sensitivity Analysis: Right-Hand Sides p. 192
Sensitivity Analysis with Excel p. 192
Integer Programming p. 196
Overview of Integer Programming p. 196
Example: Southern General Hospital p. 197
The Zero--One Model p. 200
Example: The Fixed-Charge Situation p. 201
Detailed Modeling Example p. 203
Opportunity/Problem Recognition p. 203
Model Formulation p. 203
Data Collection p. 203
Analysis of the Model p. 205
Implementation p. 208
Questions p. 210
Experiential Exercise p. 211
Modeling Exercises p. 211
Case: The Daphne Jewelry Company p. 217
Case: Hensley Valve Corp. (A) p. 219
Case: Hensley Valve Corp. (B) p. 219
Bibliography p. 220
Decision Analysis p. 221
The Modeling Process for Decision Analysis Studies p. 222
The Modeling Process p. 223
Structure of the Chapter p. 224
The Decision Analysis Situation p. 224
Mary's Dilemma p. 224
The Structure of Decision Tables p. 225
Classification of Decision Situations p. 228
Decisions Under Certainty p. 228
Complete Enumeration p. 229
Example: Assignment of Employees to Machines p. 229
Computation with Analytical Models p. 230
Decisions Under Uncertainty p. 230
Equal Probabilities (Laplace) Criterion p. 231
Pessimism (Maximin or Minimax) Criterion p. 231
Optimism (Maximax or Minimin) Criterion p. 232
Coefficient of Optimism (Hurwicz) Criterion p. 233
Regret (Savage) Criterion p. 237
Decisions Under Risk p. 237
Objective and Subjective Probabilities p. 238
Solution Procedures to Decision Making Under Risk p. 238
Notes on Implementation p. 242
Sensitivity Analysis p. 242
Decision Trees for Risk Analysis p. 243
Structure of a Decision Tree p. 243
Evaluating a Decision Tree p. 245
The Multiperiod, Sequential Decision Case p. 246
The Value of Additional Information p. 250
Information Quality: Perfect Versus Imperfect Information p. 250
The Value of Perfect Information p. 251
Imperfect Information and Bayes' Theorem p. 253
Bayes' Theorem p. 253
Using Revised Probabilities with Imperfect Information p. 254
Calculating Revised Probabilities p. 259
Computing the Revised Probabilities p. 260
Detailed Modeling Example p. 262
Opportunity/Problem Recognition p. 262
Model Formulation p. 262
Data Collection p. 263
Analysis of the Model p. 263
Implementation p. 265
Questions p. 270
Experiential Exercises p. 270
Modeling Exercises p. 271
Case: Maintaining the Water Valves p. 276
Case: The Air Force Contract p. 277
Endnotes p. 278
Bibliography p. 278
Queuing Theory p. 279
The Modeling Process for Queuing Studies p. 282
Opportunity/Problem Recognition p. 282
Model Formulation p. 282
Data Collection p. 283
Analysis of the Model p. 283
Implementation p. 283
The Queuing Situation p. 284
Characteristics of Waiting Line Situations p. 284
The Structure of a Queuing System p. 285
The Managerial Problem p. 286
The Costs Involved in a Queuing Situation p. 287
Modeling Queues p. 288
Queuing Model Notation p. 288
Deterministic Queuing Systems p. 289
The Arrival Process p. 290
The Service Process p. 292
Measures for the Service p. 293
The Waiting Line p. 294
Analysis of the Basic Queue (M/M/1 FCFS/[infinity]/[infinity]) p. 295
Poisson-Exponential Model Characteristics p. 295
Measure of Performance (Operating Characteristics) p. 296
Managerial Use of the Measures of Performance p. 298
Using Excel's Goal Seek Function p. 298
More Complex Queuing Situations p. 298
Multifacility Queuing Systems (M/M/K FCFS/[infinity]/[infinity]) p. 299
Example: Multichannel Queue p. 301
Example: Multichannel Queue at Macro-Market p. 301
Serial (Multiphase) Queues p. 304
Example: Serial Queue--Three-Station Process p. 304
Detailed Modeling Example p. 306
Opportunity/Problem Recognition p. 306
Model Formulation p. 306
Data Collection p. 306
Analysis of the Model p. 307
Implementation p. 308
Questions p. 309
Experiential Exercise p. 310
Modeling Exercises p. 310
Case: City of Help p. 315
Case: Newtown Maintenance Division p. 315
Bibliography p. 316
Simulation p. 317
General Overview of Simulation p. 319
Types of Simulation p. 320
Uses of Simulation p. 322
Advantages and Disadvantages of Simulation p. 322
The Modeling Process for Monte Carlo Simulation p. 323
Opportunity/Problem Recognition p. 323
Model Formulation p. 323
Data Collection p. 324
Analysis of the Model p. 324
Implementation p. 327
The Monte Carlo Methodology p. 327
The Tourist Information Center p. 327
Simulation Terminology p. 328
Generating Random Variates in the Monte Carlo Process p. 330
Time Independent, Discrete Simulation p. 332
Example: Marvin's Service Station p. 333
Solution by Simulation p. 333
Time Dependent Simulation p. 339
Simulation Analysis with Discrete Distributions p. 240
Simulation with Continuous Probability Distributions p. 342
Risk Analysis p. 342
Detailed Modeling Example p. 344
Opportunity/Problem Recognition p. 344
Model Formulation and Data Collection p. 344
Analysis of the Model p. 347
Implementation p. 348
Crystal Ball 2000 p. 350
Questions p. 350
Experiential Exercise p. 359
Modeling Exercises p. 360
Case: Medford Delivery Service p. 366
Case: Warren Lynch's Retirement p. 366
Case: Cartron, Inc. p. 369
Endnotes p. 371
Bibliography p. 371
Implementation and Project Management p. 372
Implementation and Project Modeling p. 373
The Project Modeling Process p. 373
Structure of the Chapter p. 374
Implementing the Modeling Study p. 375
Soft Aspects p. 375
Rational Issues and Reconsideration p. 377
The Role of Project Management p. 378
Example: Moose Lake p. 378
Planning the Project p. 381
Analysis of the Project p. 382
Sequence the Activities p. 382
Estimate Activity Times and Costs p. 382
Scheduling the Project p. 383
Construct the Network p. 383
Event Analysis p. 385
PERT/CPM Network Characteristics p. 391
Estimating Activity Times in PERT p. 393
Finding the Probabilities of Completion in PERT p. 394
Example: Finding the Probability of Completion within a Desired Time, D p. 397
Example: Finding the Duration Associated with a Desired Probability p. 399
Determining the Distribution of Project Completion Times with Simulation p. 399
Step 6: Monitoring and Controlling the Project p. 403
Monitoring the Project p. 403
Controlling the Project p. 403
Example: Resource Allocation Schedule p. 405
Critical Path Method (CPM): Cost-Time Trade-Offs p. 406
Example: Finding the Least-Cost Plan p. 409
Example: Least-Cost Plan for 22 Days p. 441
Analyzing Cost-Time Trade-Offs with Excel's Solver p. 314
Detailed Modeling Example p. 418
Opportunity/Problem Recognition p. 418
Model Formulation p. 418
Data Collection p. 421
Analysis of the Model p. 423
Implementation p. 424
Questions p. 426
Experiential Exercise p. 426
Modeling Exercises p. 426
Case: NutriTech p. 431
Case: Dart Investments p. 432
Bibliography p. 433
Mathematics p. 435
Tables p. 441
Index p. 451
About the Authors p. xxiii
Decision Making and Quantitative Modeling p. 1
Quantitative Business Modeling p. 7
Definition of a Model p. 9
Benefits and Drawbacks of Modeling p. 10
Types of Models p. 11
Effective Modelers p. 14
The Modeling Process p. 14
A Five-Step Modeling Process p. 16
Opportunity/Problem Recognition p. 17
Model Formulation p. 17
Data Collection p. 21
Analysis of the Model p. 23
Implementation and Project Management p. 25
Detailed Modeling Example p. 28
Opportunity/Problem Recognition p. 28
Model Formulation p. 29
Data Collection p. 30
Analysis of the Model p. 30
Implementation and Project Management p. 30
Software for Modeling p. 33
Questions p. 33
Experiential Exercises p. 34
Modeling Exercises p. 35
Case: Henry Ford Hospital p. 36
Endnotes p. 37
Bibliography p. 37
Data Collection and Analysis p. 38
Data Collection p. 39
Summarizing Data p. 42
Descriptive Statistics p. 42
Statistical Displays p. 44
Probability and Random Variables p. 47
Subjective Probablility p. 48
Logical Probability p. 48
Experimental Probability p. 48
Event Relationships and Probability Laws p. 48
Probability Distributions p. 51
Common Probability Distributions p. 52
The Binomial Distribution p. 53
The Poisson Distribution p. 54
The Exponential Distribution p. 55
The Normal Distribution p. 56
The t Distribution p. 58
Distributions of Sample Statistics p. 58
Chi-Square Goodness of Fit Test p. 60
Point and Interval Estimation p. 64
Interval Estimation of a Mean p. 65
Determining the Size of the Sample for a Normal Distribution p. 68
Interval Estimation and Determination of Sample Size for a Proportion p. 69
Hypothesis Testing p. 71
Hypothesis Tests for Means p. 73
Comparing Multiple Means--Analysis of Variance (ANOVA) p. 77
Detailed Modeling Example p. 81
Opportunity/Problem Recognition p. 81
Model Formulation p. 82
Data Collection p. 82
Analysis of the Model p. 85
Implementation p. 86
Questions p. 89
Experiential Exercise p. 89
Modeling Exercises p. 90
Case: Fiberease Inc. p. 93
Case: InterAccess Inc. p. 95
Case: eApp Inc. p. 95
Endnote p. 96
Bibliography p. 96
Statistical Models: Regression and Forecasting p. 97
The Modeling Process for Statistical Studies p. 99
The Simple Linear Regression Model p. 100
Calculating the Regression Model Parameters p. 103
The Coefficient of Determination and the Correlation Coefficient p. 105
Regression Analysis Assumptions p. 109
Using the Regression Model p. 110
The Multiple Regression Model p. 112
Developing Regression Models p. 115
Identify Candidate Independent Variables to Include in the Model p. 115
Transform the Data p. 117
Select the Variables to Include in the Model p. 118
Analyze the Residuals p. 118
Regression Hypothesis Tests p. 119
Time Series Analysis p. 121
Components of a Time Series p. 121
Time Series Models p. 123
Detailed Modeling Example p. 130
Opportunity/Problem Recognition p. 130
Model Formulation p. 131
Data Collection p. 131
Analysis of the Model p. 131
Implementation p. 135
Questions p. 140
Experiential Exercise p. 140
Modeling Exercises p. 141
Case: Resale Value of Long's Automobile p. 144
Case: Lewisville Crate Company p. 144
Bibliography p. 147
Optimization and Mathematical Programming p. 148
The Modeling Process for Optimization Studies p. 153
Optimization p. 153
The Modeling Process p. 154
Structure of the Chapter p. 156
Linear Programming p. 156
The Output-Mix Problem p. 157
The Blending Problem p. 157
Formulating the Linear Programming Model p. 157
Output-Mix and Blending Problems: Two Examples p. 158
Example: The Blending (Minimization) Problem p. 160
The General LP Model p. 161
Advantages, Assumptions, and Solution Methods p. 162
Distribution Problems; Transportation, Transshipment, Assignment p. 164
Analysis of the Model by the Graphical Method p. 165
A Maximization Problem p. 165
A Minimization Problem p. 172
Utilization of the Resources--Slack and Surplus Variables p. 174
Special Situations p. 175
Solving Linear Programming Models with Excel p. 177
Using Excel's Solver p. 177
Solving Large Problems p. 181
Back to Startron's Dilemma p. 185
Sensitivity ("What-If") Analysis p. 189
Why a Sensitivity Analysis? p. 189
Sensitivity Analysis: Objective Function p. 190
Sensitivity Analysis: Right-Hand Sides p. 192
Sensitivity Analysis with Excel p. 192
Integer Programming p. 196
Overview of Integer Programming p. 196
Example: Southern General Hospital p. 197
The Zero--One Model p. 200
Example: The Fixed-Charge Situation p. 201
Detailed Modeling Example p. 203
Opportunity/Problem Recognition p. 203
Model Formulation p. 203
Data Collection p. 203
Analysis of the Model p. 205
Implementation p. 208
Questions p. 210
Experiential Exercise p. 211
Modeling Exercises p. 211
Case: The Daphne Jewelry Company p. 217
Case: Hensley Valve Corp. (A) p. 219
Case: Hensley Valve Corp. (B) p. 219
Bibliography p. 220
Decision Analysis p. 221
The Modeling Process for Decision Analysis Studies p. 222
The Modeling Process p. 223
Structure of the Chapter p. 224
The Decision Analysis Situation p. 224
Mary's Dilemma p. 224
The Structure of Decision Tables p. 225
Classification of Decision Situations p. 228
Decisions Under Certainty p. 228
Complete Enumeration p. 229
Example: Assignment of Employees to Machines p. 229
Computation with Analytical Models p. 230
Decisions Under Uncertainty p. 230
Equal Probabilities (Laplace) Criterion p. 231
Pessimism (Maximin or Minimax) Criterion p. 231
Optimism (Maximax or Minimin) Criterion p. 232
Coefficient of Optimism (Hurwicz) Criterion p. 233
Regret (Savage) Criterion p. 237
Decisions Under Risk p. 237
Objective and Subjective Probabilities p. 238
Solution Procedures to Decision Making Under Risk p. 238
Notes on Implementation p. 242
Sensitivity Analysis p. 242
Decision Trees for Risk Analysis p. 243
Structure of a Decision Tree p. 243
Evaluating a Decision Tree p. 245
The Multiperiod, Sequential Decision Case p. 246
The Value of Additional Information p. 250
Information Quality: Perfect Versus Imperfect Information p. 250
The Value of Perfect Information p. 251
Imperfect Information and Bayes' Theorem p. 253
Bayes' Theorem p. 253
Using Revised Probabilities with Imperfect Information p. 254
Calculating Revised Probabilities p. 259
Computing the Revised Probabilities p. 260
Detailed Modeling Example p. 262
Opportunity/Problem Recognition p. 262
Model Formulation p. 262
Data Collection p. 263
Analysis of the Model p. 263
Implementation p. 265
Questions p. 270
Experiential Exercises p. 270
Modeling Exercises p. 271
Case: Maintaining the Water Valves p. 276
Case: The Air Force Contract p. 277
Endnotes p. 278
Bibliography p. 278
Queuing Theory p. 279
The Modeling Process for Queuing Studies p. 282
Opportunity/Problem Recognition p. 282
Model Formulation p. 282
Data Collection p. 283
Analysis of the Model p. 283
Implementation p. 283
The Queuing Situation p. 284
Characteristics of Waiting Line Situations p. 284
The Structure of a Queuing System p. 285
The Managerial Problem p. 286
The Costs Involved in a Queuing Situation p. 287
Modeling Queues p. 288
Queuing Model Notation p. 288
Deterministic Queuing Systems p. 289
The Arrival Process p. 290
The Service Process p. 292
Measures for the Service p. 293
The Waiting Line p. 294
Analysis of the Basic Queue (M/M/1 FCFS/[infinity]/[infinity]) p. 295
Poisson-Exponential Model Characteristics p. 295
Measure of Performance (Operating Characteristics) p. 296
Managerial Use of the Measures of Performance p. 298
Using Excel's Goal Seek Function p. 298
More Complex Queuing Situations p. 298
Multifacility Queuing Systems (M/M/K FCFS/[infinity]/[infinity]) p. 299
Example: Multichannel Queue p. 301
Example: Multichannel Queue at Macro-Market p. 301
Serial (Multiphase) Queues p. 304
Example: Serial Queue--Three-Station Process p. 304
Detailed Modeling Example p. 306
Opportunity/Problem Recognition p. 306
Model Formulation p. 306
Data Collection p. 306
Analysis of the Model p. 307
Implementation p. 308
Questions p. 309
Experiential Exercise p. 310
Modeling Exercises p. 310
Case: City of Help p. 315
Case: Newtown Maintenance Division p. 315
Bibliography p. 316
Simulation p. 317
General Overview of Simulation p. 319
Types of Simulation p. 320
Uses of Simulation p. 322
Advantages and Disadvantages of Simulation p. 322
The Modeling Process for Monte Carlo Simulation p. 323
Opportunity/Problem Recognition p. 323
Model Formulation p. 323
Data Collection p. 324
Analysis of the Model p. 324
Implementation p. 327
The Monte Carlo Methodology p. 327
The Tourist Information Center p. 327
Simulation Terminology p. 328
Generating Random Variates in the Monte Carlo Process p. 330
Time Independent, Discrete Simulation p. 332
Example: Marvin's Service Station p. 333
Solution by Simulation p. 333
Time Dependent Simulation p. 339
Simulation Analysis with Discrete Distributions p. 240
Simulation with Continuous Probability Distributions p. 342
Risk Analysis p. 342
Detailed Modeling Example p. 344
Opportunity/Problem Recognition p. 344
Model Formulation and Data Collection p. 344
Analysis of the Model p. 347
Implementation p. 348
Crystal Ball 2000 p. 350
Questions p. 350
Experiential Exercise p. 359
Modeling Exercises p. 360
Case: Medford Delivery Service p. 366
Case: Warren Lynch's Retirement p. 366
Case: Cartron, Inc. p. 369
Endnotes p. 371
Bibliography p. 371
Implementation and Project Management p. 372
Implementation and Project Modeling p. 373
The Project Modeling Process p. 373
Structure of the Chapter p. 374
Implementing the Modeling Study p. 375
Soft Aspects p. 375
Rational Issues and Reconsideration p. 377
The Role of Project Management p. 378
Example: Moose Lake p. 378
Planning the Project p. 381
Analysis of the Project p. 382
Sequence the Activities p. 382
Estimate Activity Times and Costs p. 382
Scheduling the Project p. 383
Construct the Network p. 383
Event Analysis p. 385
PERT/CPM Network Characteristics p. 391
Estimating Activity Times in PERT p. 393
Finding the Probabilities of Completion in PERT p. 394
Example: Finding the Probability of Completion within a Desired Time, D p. 397
Example: Finding the Duration Associated with a Desired Probability p. 399
Determining the Distribution of Project Completion Times with Simulation p. 399
Step 6: Monitoring and Controlling the Project p. 403
Monitoring the Project p. 403
Controlling the Project p. 403
Example: Resource Allocation Schedule p. 405
Critical Path Method (CPM): Cost-Time Trade-Offs p. 406
Example: Finding the Least-Cost Plan p. 409
Example: Least-Cost Plan for 22 Days p. 441
Analyzing Cost-Time Trade-Offs with Excel's Solver p. 314
Detailed Modeling Example p. 418
Opportunity/Problem Recognition p. 418
Model Formulation p. 418
Data Collection p. 421
Analysis of the Model p. 423
Implementation p. 424
Questions p. 426
Experiential Exercise p. 426
Modeling Exercises p. 426
Case: NutriTech p. 431
Case: Dart Investments p. 432
Bibliography p. 433
Mathematics p. 435
Tables p. 441
Index p. 451
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