Data analysis, optimization, and simulation modeling:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[Mason, Ohio]
South-Western, Cengage Learning
2011
|
| Edition: | 4. ed., internat. ed. |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | 4. US ed. u.d.T.: Albright, Samuel Christian: Data analysis and decision making |
| Physical Description: | XVI, 1061 S. Ill., graph. Darst. Access code |
| ISBN: | 0538476761 9780538476768 |
Staff View
MARC
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| 245 | 1 | 0 | |a Data analysis, optimization, and simulation modeling |c S. Christian Albright ; Christopher J. Zappe ; Wayne L. Winston |
| 250 | |a 4. ed., internat. ed. | ||
| 264 | 1 | |a [Mason, Ohio] |b South-Western, Cengage Learning |c 2011 | |
| 300 | |a XVI, 1061 S. |b Ill., graph. Darst. |e Access code | ||
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| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a 4. US ed. u.d.T.: Albright, Samuel Christian: Data analysis and decision making | ||
| 700 | 1 | |a Zappe, Christopher J. |e Verfasser |4 aut | |
| 700 | 1 | |a Winston, Wayne L. |d 1950- |e Verfasser |0 (DE-588)134241614 |4 aut | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-024731955 | ||
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| _version_ | 1804148814758543360 |
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| adam_text | Titel: Data analysis, optimization, and simulation modeling
Autor: Albright, Samuel Christian
Jahr: 2011
Contents
Preface xii 2.7 Excel Tables for Filtering, Sorting, and
I Introduction to Data Analysis and 2.7.1 Filtering 70
Decision Making I
2.8 Conclusion 75
CASE 2.1 Correct Interpretation of Means 81
CASE 2.2 The Dow Jones Industrial Average 82
CASE 2.3 Home and Condo Prices 83
1.1 Introduction 2
1.2 An Overview of the Book 4
1.2.1 The Methods 4
1.2.2 The Software 7
1.3 Modeling and Models II 3 Finding Relationships among Variables 85
1.3.1 Graphical Models II 3.1 Introduction 87
1.3.2 Algebraic Models 12 3.2 Relationships among Categorical Variables 88
1.3.3 Spreadsheet Models 12 3.3 Relationships among Categorical Variables
1.3.4 A Seven-Step Modeling Process 14 ™* a Numerical Variable 92
1.4 Conclusion 16 3,3_ I Stacked and Unstacked Formats 93
CASE I. I Entertainment on a Cruise Ship 17 3.4 Relationships among Numerical Variables 101
PART I Exploring Data 19
3.4.1 Scatterplots 102
3.4.2 Correlation and Covariance 106
3.5 Pivot Tables 114
Describing the Distribution of a U An Extended ExamPle l37
Single Variable 21 3.7 Conclusion 144
2.1 Introduction 23 CASE 3.1 Customer Arrivals at Bank98 149
2.2 Basic Concepts 24 CASE 3-2 Savin SPendin and Social
2.2.1 Populations and Samples 24
CASE 3.3 Churn in the Cellular Phone
PART 2
Market 151
Probability and Decision
Making under
Uncertainty 153
2.2.2 Data Sets, Variables, and Observations 25
2.2.3 Types of Data 27
2.3 Descriptive Measures for Categorical
Variables 30
2.4 Descriptive Measures for Numerical
Variables 33
2.4.1 Numerical Summary Measures 34 4 Probability and Probability Distributions 155
2.4.2 Numerical Summary Measures with 4.1 Introduction 156
StatTools 43 4.2 Probability Essentials 158
2.4.3 Charts for Numerical Variables 48 4.2.1 Rule 0f Complements 159
2.5 Time Series Data 57 4.2.2 Addition Rule 159
2.6 Outliers and Missing Values 64 4.2.3 Conditional Probability and the
2.6.1 Outliers 64 Multiplication Rule 160
2.6.2 Missing Values 65 4.2.4 Probabilistic Independence 162
4.2.5 Equally Likely Events 163 5.8 Conclusion 261
4.2.6 Subjective Versus Objective CASE 5.1 EuroWatch Company 269
Probabilities 163 CASE 5.2 Cashing in on the Lottery 270
4.3 Distribution of a Single Random Variable 166
4.3.1 Conditional Mean and Variance 170 6 Decision Making under Uncertainty 273
4.4 An Introduction to Simulation 173 6J Introduction 274
4.5 Distribution of Two Random Variables: Scenario 6-2 Elements of Decision Analvsis 276
Approach 177 6.2.1 Payoff Tables 276
4.6 Distribution of Two Random Variables: Joint 6.2.2 Possible Decision Criteria 277
Probability Approach 183 6.2.3 Expected Monetary Value (EMV) 278
4.6.1 How to Assess Joint Probability 6.2.4 Sensitivity Analysis 280
Distributions 187 6.2.5 Decision Trees 280
4.7 Independent Random Variables 189 6.2.6 Risk Profiles 282
4.8 Weighted Sums of Random Variables 193 6.3 The PrecisionTree Add-In 290
4.9 Conclusion 200 6.4 Bayes Rule 303
CASE 4.1 Simpson s Paradox 208 6.5 Multistage Decision Problems 307
Normal, Binomial, Poisson, and Exponential 6.5.1 The Value of Information 311
Distributions 209 6.6 Incorporating Attitudes Toward Risk 323
5.1 Introduction 211 6.6.1 Utility Functions 324
5.2 The Normal Distribution 211 6.6.2 Exponential Utility 324
5.2.1 Continuous Distributions and 6.6.3 Certainty Equivalents 328
Density Functions 211 6.6.4 Is Expected Utility Maximization
5.2.2 The Normal Density 213 Used? 330
5.2.3 Standardizing:/-Values 214 6.7 Conclusion 331
5.2.4 Normal Tables and /-Values 216 CASE 6.1 Jogger Shoe Company 345
5.2.5 Normal Calculations in Excel 217 CASE 6.2 Westhouser Parer Company 346
5.2.6 Empirical Rules Revisited 220 CASE 6.3 Biotechnical Engineering 347
5.3 Applications of the Normal Distribution 221
5.4 The Binomial Distribution 233
5.4.1 Mean and Standard Deviation of the
Binomial Distribution 236
5.4.2 The Binomial Distribution in the
Context of Sampling 236 7 Sampling and Sampling Distributions 351
5.4.3 The Normal Approximation to the 7.1 lntroduction 352
Binomial 237 7-2 Sampling Terminology 353
5.5 Applications of the Binomial Distribution 238 7-3 Methot|s f°r Selecting Random Samples 354
5.6 The Poisson and Exponential Distributions 250 7.3.1 Simple Random Sampling 354
5.6.1 The Poisson Distribution 250 7.3.2 Systematic Sampling 360
5.6.2 The Exponential Distribution 252 7.3.3 Stratified Sampling 361
5.7 Fitting a Probability Distribution to Data with 7.3.4 Cluster Sampling 364
©RISK 255 7.3.5 Multistage Sampling Schemes 365
PART 3 Statistical Inference 349
7.4 An Introduction to Estimation 366 9.2 Concepts in Hypothesis Testing 457
7.4.1 Sources of Estimation Error 367 9.2.1 Null and Alternative Hypotheses 458
7.4.2 Key Terms in Sampling 368 9.2.2 One-Tailed Versus Two-Tailed Tests 459
7.4.3 Sampling Distribution of the Sample 9.2.3 Types of Errors 459
Mean 369 9.2.4 Significance Level and Rejection
7.4.4 The Central Limit Theorem 374 Region 460
7.4.5 Sample Size Determination 379 9.2.5 Significance from /rvalues 461
7.4.6 Summary of Key Ideas for Simple Random 9.2.6 Type II Errors and Power 462
Sampling 380 9.2.7 Hypothesis Tests and Confidence
7.5 Conclusion 382 Intervals 463
CASE 7.1 Sampling from DVD Movie Renters 386 9.2.8 Practical Versus Statistical Significance 463
9.3 Hypothesis Tests for a Population Mean 464
Confidence Interval Estimation 387 9.4 Hypothesis Tests for Other Parameters 472
8.1 Introduction 388 9.4.1 Hypothesis Tests for a Population
8.2 Sampling Distributions 390 Proportion 472
8.2.1 The t Distribution 390 9.4.2 Hypothesis Tests for Differences between
8.2.2 Other Sampling Distributions 393 Population Means 475
8.3 Confidence Interval for a Mean 394 9.4.3 Hypothesis Test for Equal Population
8.4 Confidence Interval for a Total 400 Variances 485
8.5 Confidence Interval for a Proportion 403 9.4.4 Hypothesis Tests for Differences between
8.6 Confidence Interval for a Standard Population Proportions 486
Deviation 409 9.5 Tests for Normality 494
8.7 Confidence Interval for the Difference 9.6 Chi-Square Test for Independence 500
between Means 412 9.7 One-Way ANOVA 505
8.7.1 Independent Samples 413 9.8 Conclusion 513
8.7.2 Paired Samples 421 CASE 9.1 Regression Toward the Mean 519
8.8. Confidence Interval for the Difference between CASE 9.2 Baseball Statistics 520
Proportions 427 CASE 9.3 The Wichita Anti-Drunk Driving
8.9. Controlling Confidence Interval Length 433 Advertising Campaign 521
8.9.1 Sample Size for Estimation of the CASE 9.4 Deciding Whether to Switch to a
Mean 434 New Toothpaste Dispenser 523
8.9.2 Sample Size for Estimation of CASE 9.5 Removing Vioxx from the Market 526
Other Parameters 436
8.10 Conclusion 441
CASE 8.1 Harrigan University Admissions 449
CASE 8.2 Employee Retention at D Y 450
CASE 8.3 Delivery Times at SnowPea
Restaurant 451
CASE 8.4 The Bodfish Lot Cruise 452
10 Regression Analysis: Estimating
Hypothesis Testing 455 Relationships 529
9.1 Introduction 456 10.1 Introduction 531
Regression Analysis
PART 4 and Time Series
Forecasting 527
10.2 Scatterplots: Graphing Relationships 533 11.4 Multicollinearity 616
10.2.1 Linear Versus Nonlinear Relationships 538 11.5 Include/Exclude Decisions 620
10.2.2 Outliers 538 11.6 Stepwise Regression 625
10.2.3 Unequal Variance 539 11.7 The Partial F Test 630
10.2.4 No Relationship 540 11.8 Outliers 638
10.3 Correlations: Indicators of Linear 11.9 Violations of Regression Assumptions 644
Relationships 540 11.9.1 Nonconstant Error Variance 644
10.4 Simple Linear Regression 542 M 92 Nonnormality of Residuals 645
10.4.1 Least Squares Estimation 542 11.9.3 Autocorrelated Residuals 645
10.4.2 Standard Error of Estimate 549 11.10 Prediction 648
10.4.3 The Percentage of Variation ,, 11.11 Conclusion 653
Explained:R2 550 CASE 11.1 The Artsy Corporation 663
10.5 Multiple Regression 553 CASE ,, 2 Heating Oil at Dupree Fuels
10.5.1 Interpretation of Regression Coefficients 554 Company 665
10.5.2 Interpretation of Standard Error of CASE ,, 3 Developing a F|exib|e Budget at
Estimate and R 556 the Gunderson P|ant 666
10.6 Modeling Possibilities 560 CASE ,, 4 Forecasting Overhead at Wagner
10.6.1 Dummy Variables 560 Printers 667
10.6.2 Interaction Variables 566
10.6.3 Nonlinear Transformations 571 l2 Time SerieS AnalysiS and Forecasting 669
10.7 Validation of the Fit 586 l2J lntroductlon 671
10.8 Conclusion 588 12.2 Forecasting Methods: An Overview 671
CASE I O.I Quantity Discounts at the Firm 12.2.1 Extrapolation Methods 672
Chair Company 596 llu Econometric Models 672
CASE 10.2 Housing Price Structure in 12-2.3 Combining Forecasts 673
Mid City 597 12.2.4 Components of Time Series
CASE 10.3 Demand for French Bread at Data 673
Howie s Bakery 598 12.2.5 Measures of Accuracy 676
CASE 10.4 Investing for Retirement 599 12.3 Testing for Randomness 678
12.3.1 The Runs Test 681
II Regression Analysis: Statistical Inference 601 12.3.2 Autocorrelation 683
I I.I Introduction 603 12.4 Regression-Based Trend Models 687
11.2 The Statistical Model 603 12.4.1 Linear Trend 687
11.3 Inferences about the Regression 12.4.2 Exponential Trend 690
Coefficients 607 12.5 The Random Walk Model 695
11.3.1 Sampling Distribution of the Regression 2-6 Autoregression Models 699
Coefficients 608 12.7 Moving Averages 704
11.3.2 Hypothesis Tests for the Regression 12.8 Exponential Smoothing 710
Coefficients and p-Values 610 12.8.1 Simple Exponential Smoothing 710
11.3.3 A Test for the Overall Fit: The ANOVA 12.8.2 Holt s Model for Trend 715
Table 611 12.9 Seasonal Models 720
PART 5
Optimization and
Simulation Modeling 743
12.9.1 Winters Exponential Smoothing 14 Optimization Models 811
Model 721 14. | Introduction 812
12.9.2 Deseasonalizing: The Ratio-to-Moving- 14.2 Worker Scheduling Models 813
Averages Method 725 14.3 Blending Models 821
12.9.3 Estimating Seasonality with Regression 729 14.4 Logistics Models 828
12.10 Conclusion 735 14.4.1 Transportation Models 828
CASE 12.1 Arrivals at the Credit Union 740 14.4.2 0ther Logjstks Modfils 837
CASE 12.2 Forecasting Weekly Sales at 14.5 Aggregate p|anning Mode,s 848
Amanta 741 14.6 Financial Models 857
14.7 Integer Programming Models 868
14.7.1 Capital Budgeting Models 869
14.7.2 Fixed-Cost Models 875
14.7.3 Set-Covering Models 883
Introduction to Optimization Modeling 745 14.8 Nonlinear Programming Models 891
13.1 Introduction 746 14.8.1 Basic Ideas of Nonlinear
13.2 Introduction to Optimization 747 Optimization 891
13.3 A Two-Variable Product Mix Model 748 14.8.2 Managerial Economics Models 891
13.4 Sensitivity Analysis 761 14.8.3 Portfolio Optimization Models 896
13.4.1 Solver s Sensitivity Report 761 14.9 Conclusion 905
13.4.2 SolverTable Add-In 765 CASE 14.1 Giant Motor Company 912
13.4.3 Comparison of Solver s Sensitivity Report CASE 14.2 GMS Stock Hedging 914
and SolverTable 770
13.5 Properties of Linear Models 772 15 Introduction to Simulation Modeling 917
13.5.1 Proportionality 773 15.1 Introduction 918
13.5.2 Additivity 773 15.2 Probability Distributions for Input
13.5.3 Divisibility 773 Variables 920
13.5.4 Discussion of Linear Properties 773 15.2.1 Types of Probability Distributions 921
13.5.5 Linear Models and Scaling 774 15.2.2 Common Probability Distributions 925
13.6 Infeasibility and Unboundedness 775 15.2.3 Using RISK to Explore Probability
13.6.1 Infeasibility 775 Distributions 929
13.6.2 Unboundedness 775 15.3 Simulation and the Flaw of
13.6.3 Comparison of Infeasibility and Averages 939
Unboundedness 776 15.4 Simulation with Built-in Excel Tools 942
13.7 A Larger Product Mix Model 778 15.5 Introduction to the RISK Add-in 953
13.8 A Multiperiod Production Model 786 15.5.1 RISK Features 953
13.9 A Comparison of Algebraic and Spreadsheet 15.5.2 Loading RISK 954
Models 796 15.5.3 ©RISK Models with a Single Random
13.10 A Decision Support System 796 Input Variable 954
13.11 Conclusion 799 15.5.4 Some Limitations of ©RISK 963
CASE 13.1 Shelby Shelving 807 15.5.5 R|sK Models with Several Random
CASE 13.2 Sonoma Valley Wines 809 Input Variables 964
15.6 The Effects of Input Distributions on
Results 969
PART 6 Online Bonus Material
15.6.1 Effect of the Shape of the Input 2 Using the Advanced Filter and
Distribution(s) 969 Database Functions 2-|
15.6.2 Effect of Correlated Input
Variables 972 7 Importing Data into Excel 17-1
15.7 Conclusion 978 l7- Introduction 17-3
CASE 15.1 Ski Jacket Production 985 l7-2 Rearranging Excel Data 17-4
CASE 15.2 Ebony Bath Soap 986 l7-3 lmPortin2 Text Data l7 8
17.4 Importing Relational Database
Simulation Models 987 Data l7- 4
17.4.1 A Brief Introduction to Relational
16.1 Introduction 989
16.2 Operations Models 989
16.2.1 Bidding for Contracts 989
16.2.2 Warranty Costs 993
Databases 17-14
17.4.2 Using Microsoft Query 17-15
17.4.3 SQL Statements 17-28
17.5 Web Queries 17-30
16.2.3 Drug Production with Uncertain
Yj Ij oqo 17.6 Cleansing Data 17-34
16.3 Financial Models 1004 l7J Conc,usion l7 42
16.3.1 Financial Planning Models 1004 CASE 17.1 EduToys, Inc. 17-46
16.3.2 Cash Balance Models 1009 Appendix A: Statistical Reporting A-1
16.3.3 Investment Models 1014 A. I Introduction A-1
16.4 Marketing Models 1020 A2 Suggestions for Good Statistical
16.4.1 Models of Customer Loyalty 1020 Reporting A-2
16.4.2 Marketing and Sales Models 1030 A.2.1 Planning A-2
16.5 Simulating Games of Chance 1036 A.2.2 Developing a Report A-3
16.5.1 Simulating the Game of Craps 1036 A.2.3 Be Clear A-4
16.5.2 Simulating the NCAA Basketball A.2.4 Be Concise A-5
Tournament 1039 Be Precise A-5
16.6 An Automated Template for RISK Examp|es 0f Statistical Reports A-6
Models l044 A.4 Conclusion A-18
16.7 Conclusion 1045
CASE 16.1 College Fund Investment 1053 References 1055
CASE 16.2 Bond Investment Strategy 1054 Index 1059
|
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| author | Albright, Samuel Christian 1946- Zappe, Christopher J. Winston, Wayne L. 1950- |
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| edition | 4. ed., internat. ed. |
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| id | DE-604.BV039872665 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:13:07Z |
| institution | BVB |
| isbn | 0538476761 9780538476768 |
| language | English |
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| publishDate | 2011 |
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| publishDateSort | 2011 |
| publisher | South-Western, Cengage Learning |
| record_format | marc |
| spelling | Albright, Samuel Christian 1946- Verfasser (DE-588)137339968 aut Data analysis, optimization, and simulation modeling S. Christian Albright ; Christopher J. Zappe ; Wayne L. Winston 4. ed., internat. ed. [Mason, Ohio] South-Western, Cengage Learning 2011 XVI, 1061 S. Ill., graph. Darst. Access code txt rdacontent n rdamedia nc rdacarrier 4. US ed. u.d.T.: Albright, Samuel Christian: Data analysis and decision making Zappe, Christopher J. Verfasser aut Winston, Wayne L. 1950- Verfasser (DE-588)134241614 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024731955&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Albright, Samuel Christian 1946- Zappe, Christopher J. Winston, Wayne L. 1950- Data analysis, optimization, and simulation modeling |
| title | Data analysis, optimization, and simulation modeling |
| title_auth | Data analysis, optimization, and simulation modeling |
| title_exact_search | Data analysis, optimization, and simulation modeling |
| title_full | Data analysis, optimization, and simulation modeling S. Christian Albright ; Christopher J. Zappe ; Wayne L. Winston |
| title_fullStr | Data analysis, optimization, and simulation modeling S. Christian Albright ; Christopher J. Zappe ; Wayne L. Winston |
| title_full_unstemmed | Data analysis, optimization, and simulation modeling S. Christian Albright ; Christopher J. Zappe ; Wayne L. Winston |
| title_short | Data analysis, optimization, and simulation modeling |
| title_sort | data analysis optimization and simulation modeling |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024731955&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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