| 000 | 01166camuu22003138a 4500 | |
| 001 | 000000800668 | |
| 005 | 20030103165952 | |
| 008 | 020824s2002 caua 000 0 eng | |
| 015 | ▼a GBA2-V1402 | |
| 020 | ▼a 053438367X | |
| 040 | ▼a UKM ▼c UKM ▼d EYE ▼d 211009 | |
| 049 | ▼a KUBA ▼l 121068148 ▼f 과학 | |
| 082 | 0 4 | ▼a 658.403002855369 ▼2 21 |
| 090 | ▼a 658.403 ▼b W783d2 | |
| 100 | 1 | ▼a Winston, Wayne L. |
| 245 | 1 0 | ▼a Data analysis and decision making with Microsoft Excel / ▼c Wayne L. Winston, Christopher Zappe, S. Albright. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Belmont, Calif. : ▼b Duxbury ; ▼a London : ▼b Thomson Learning, ▼c 2002. | |
| 300 | ▼a xxii, 999 p. : ▼b ill. ; ▼c 26 cm + ▼e 1 computer laser optical disk(4 3/4 in.). | |
| 500 | ▼a Previous ed.: 2002. | |
| 504 | ▼a Bibliography. | |
| 504 | ▼a Includes index. | |
| 525 | ▼a Includes CD-ROM. | |
| 630 | 0 4 | ▼a Microsoft Excel (Computer file) |
| 650 | 0 | ▼a Industrial management ▼x Statistical methods ▼x Computer programs. |
| 650 | 0 | ▼a Decision making ▼x Computer programs. |
| 700 | 1 | ▼a Zappe, Christopher J. ▼q (Christopher James) , ▼d 1961- |
| 700 | 1 | ▼a Albright, S. |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 658.403 W783d2 | Accession No. 121068148 (6회 대출) | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
The emphasis of the text is on data analysis, modeling, and spreadsheet use in statistics and management science. This text contains professional Excel software add-ins. The authors maintain the elements that have made this text a market leader in its first edition: clarity of writing, a teach-by-example approach, and complete Excel integration.
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Table of Contents
CONTENTS Preface = xv Chapter 1 : Introduction to Data Analysis and Decision Making = 1 1.1 Introduction = 2 1.2 An Overview of the Book = 4 1.3 A Sampling of Examples = 10 1.4 Modeling and Models = 21 1.5 Conclusion = 26 CASE 1.1 : Entertainment on a Cruise Ship = 27 Part 1 Getting, Describing, and Summarizing Data Chapter 2 : Describing Data : Graphs and Tables = 29 2.1 Introduction = 31 2.2 Basic Concepts = 32 2.3 Frequency Tables and Histograms = 36 2.4 Analyzing Relationships with Scatterplots = 47 2.5 Time Series Plots = 51 2.6 Exploring Data with Pivot Tables = 55 2.7 Conclusion = 67 CASE 2.1 : Customer Arrivals at Bank98 = 73 CASE 2.2 : Automobile Production and Purchases = 73 CASE 2.3 : Saving, Spending, and Social Climbing = 74 Chapter 3 : Describing Data : Summary Measures = 75 3.1 Introduction = 76 3.2 Measures of Central Location = 78 3.3 Quartiles and Percentiles = 80 3.4 Minimum, Maximum, and Range = 81 3.5 Measures of Variability : Variance and Standard Deviation = 82 3.6 Obtaining Summary Measures with Add-Ins = 87 3.7 Measures of Association : Covariance and Correlation = 91 3.8 Describing Data Sets with Boxplots = 95 3.9 Applying the Tools = 100 3.10 Conclusion = 117 CASE 3.1 : The Dow Jones Averages = 125 CASE 3.2 : Other Market Indexes = 127 CASE 3.3 : Correct Interpretation of Means = 128 Chapter 4 : Getting the Right Data = 129 4.1 Introduction = 130 4.2 Sources of Data = 131 4.3 Using Excel's AutoFilter = 134 4.4 Complex Queries with the Advanced Filter = 140 4.5 Importing External Data from Access = 146 4.6 Creating Pivot Tables from External Data = 158 4.7 Web Queries = 160 4.8 Other Data Sources on the Web = 170 4.9 Cleansing the Data = 176 4.10 Conclusion = 183 CASE 4.1 : EduToys, Inc. = 187 Part 2 Probability, Uncertainty, and Decision Making Chapter 5 : Probability and Probability Distributions = 189 5.1 Introduction = 190 5.2 Probability Essentials = 191 5.3 Distribution of a Single Random Variable = 199 5.4 An Introduction to Simulation = 203 5.5 Distribution of Two Random Variables : Scenario Approach = 207 5.6 Distribution of Two Random Variables : Joint Probability Approach = 213 5.7 Independent Random Variables = 220 5.8 Weighted Sums of Random Variables = 224 5.9 Conclusion = 231 CASE 5.1 : Simpson's Paradox = 238 Chapter 6 : Normal, Binomial, Poisson, and Exponential Distributions = 239 6.1 Introduction = 240 6.2 The Normal Distribution = 241 6.3 Applications of the Normal Distribution = 250 6.4 The Binomial Distribution = 262 6.5 Applications of the Binomial Distribution = 266 6.6 The Poisson and Exponential Distributions = 278 6.7 Fitting a Probability Distribution to Data : BestFit = 283 6.8 Conclusion = 288 CASE 6.1 : EuroWatch Company = 296 CASE 6.2 : Cashing in on the Lottery = 297 Chapter 7 : Decision Making Under Uncertainty = 299 7.1 Introduction = 300 7.2 Elements of a Decision Analysis = 302 7.3 The PrecisionTree Add-In = 311 7.4 More Single-Stage Examples = 320 7.5 Multistage Decision Problems = 329 7.6 Bayes' Rule = 337 7.7 Incorporating Attitudes Toward Risk = 345 7.8 Conclusion 354 CASE 7.1 : Jogger Shoe Company = 363 CASE 7.2 : Westhouser Paper Company = 364 Part 3 Statistical Inference Chapter 8 : Sampling and Sampling Distributions = 365 8.1 Introduction = 366 8.2 Sampling Terminology = 366 8.3 Methods for Selecting Random Samples = 367 8.4 An Introduction to Estimation = 384 8.5 Conclusion = 404 CASE 8.1 : Sampling from Videocassette Renters = 413 Chapter 9 : Confidence Interval Estimation = 415 9.1 Introduction = 416 9.2 Sampling Distributions = 417 9.3 Confidence Interval for a Mean = 423 9.4 Confidence Interval for a Total = 429 9.5 Confidence Interval for a Proportion = 431 9.6 Confidence Interval for a Standard Deviation = 437 9.7 Confidence Interval for the Difference Between Means = 440 9.8 Confidence Interval for the Difference Between Proportions = 453 9.9 Controlling Confidence Interval Length = 458 9.10 Conclusion = 466 CASE 9.1 : Harrigan University Admissions = 474 CASE 9.2 : Employee Retention at D&Y = 475 CASE 9.3 : Delivery Times at SnowPea Restaurant = 476 CASE 9.4 : The Bodfish Lot Cruise = 477 Chapter 10 : Hypothesis Testing = 479 10.1 Introduction = 480 10.2 Concepts in Hypothesis Testing = 481 10.3 Hypothesis Tests for a Population Mean = 488 10.4 Hypothesis Tests for Other Parameters = 495 10.5 Tests for Normality = 516 10.6 Chi-Square Test for Independence = 522 10.7 One-Way ANOVA = 526 10.8 Conclusion = 534 CASE 10.1 : Regression Toward the Mean = 540 CASE 10.2 : Baseball Statistics = 541 CASE 10.3 : The Wichita Anti-Drunk Driving Advertising Campaign = 542 CASE 10.4 : Deciding Whether to Switch to a New Toothpaste Dispenser = 544 Part 4 Regression, Forecasting, and Time Series Chapter 11 : Regression Analysis : Estimating Relationships = 547 11.1 Introduction = 548 11.2 Scatterplots : Graphing Relationships = 551 11.3 Correlations : Indicators of Linear Relationships = 560 11.4 Simple Linear Regression = 562 11.5 Multiple Regression = 573 11.6 Modeling Possibilities = 579 11.7 Validation of the Fit = 606 11.8 Conclusion = 608 CASE 11.1 : Quantity Discounts at the FirmChair Company = 616 CASE 11.2 : Housing Price Structure in MidCity = 616 CASE 11.3 : Demand for French Bread at Howie's = 617 CASE 11.4 : Investing for Retirement = 618 Chapter 12 : Regression Analysis : Statistical Inference = 619 12.1 Introduction = 620 12.2 The Statistical Model = 621 12.3 Inferences About the Regression Coefficients = 625 12.4 Multicollinearity = 635 12.5 Include／Exclude Decisions = 639 12.6 Stepwise Regression = 644 12.7 The Partial F Test = 648 12.8 Outliers = 656 12.9 Violations of Regression Assumptions = 662 12.10 Prediction = 666 12.11 Conclusion = 672 CASE 12.1 : The Artsy Corporation = 683 CASE 12.2 : Heating Oil at Dupree Fuels Company = 685 CASE 12.3 : Developing a Flexible Budget at the Gunderson Plant = 686 CASE 12.4 : Forecasting Overhead at Wagner Printers = 687 Chapter 13 : Time Series Analysis and Forecasting = 689 13.1 Introduction = 690 13.2 Forecasting Methods : An Overview = 691 13.3 Testing for Randomness = 698 13.4 Regression-Based Trend Models = 705 13.5 The Random Walk Model = 714 13.6 Autoregression Models = 718 13.7 Moving Averages = 723 13.8 Exponential Smoothing = 729 13.9 Seasonal Models = 739 13.10 Conclusion = 754 CASE 13.1 : Arrivals at the Credit Union = 759 CASE 13.2 : Forecasting Weekly Sales at Amanta = 760 Part 5 Decision Modeling Chapter 14 : Introduction to Optimization Modeling = 761 14.1 Introduction = 762 14.2 A Brief History of Linear Programming = 762 14.3 Introduction to LP Modeling = 763 14.4 Sensitivity Analysis and the SolverTable Add-In = 773 14.5 The Linear Assumptions = 778 14.6 Graphical Solution Method = 780 14.7 Infeasibility and Unboundedness = 784 14.8 A Multiperiod Production Problem = 785 14.9 A Decision Support System = 792 14.10 Conclusion = 794 CASE 14.1 : Shelby Shelving = 800 Chapter 15 : Optimization Modeling : Applications = 803 15.1 Introduction = 804 15.2 Workforce Scheduling Models = 805 15.3 Blending Models = 811 15.4 Logistics Models = 817 15.5 Aggregate Planning Models = 827 15.6 Dynamic Financial Models = 835 15.7 Integer Programming Models = 840 15.8 Nonlinear Models = 855 15.9 Conclusion = 864 CASE 15.1 : Giant Motor Company = 870 CASE 15.2 : GMS Stock Hedging = 872 CASE 15.3 : Durham Asset Management = 874 Chapter 16 : Simulation Models = 877 16.1 Introduction = 878 16.2 Random Numbers = 879 16.3 Introduction to Spreadsheet Simulation = 881 16.4 Selecting Probability Distributions = 889 16.5 Simulating with @Risk = 896 16.6 Financial Planning Models = 912 16.7 Cash Balance Models = 918 16.8 Simulating Stock Prices and Options = 923 16.9 Market Share Models = 936 16.10 Simulating Correlated Values = 942 16.11 Using TopRank with @Risk for Powerful Modeling = 948 16.12 Conclusion = 957 CASE 16.1 : Ski Jacket Production = 966 CASK 16.2 : The College Fund Investment Decision = 967 CASE 16.3 : Ebony Bath Soap = 968 CASE 16.4 : Bond Investment Strategy = 969 References = 971 Appendix A : Statistical Reporting = 975 A.1 Introduction = 975 A.2 Suggestions for Good Statistical Reporting = 976 A.3 Examples of Statistical Reports = 981 A.4 Conclusion = 992 Index = 993