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외국도서
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대학교재/전문서적
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자연과학/순수과학계열
Experimental Design : With Applications in Management, Engeering, and the Sciences
2013년 9월 9일 이후 누적수치입니다.
- 저 : Berger, Paul D./ Maurer, Robert E.
- 출판사 : Duxbury
- 발행 : 2001년 08월 01일
- 쪽수 : 0
- ISBN : 9780534358228
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연관도서
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목차
| Introduction to Experimental Design | p. 1 |
| What Is Experimentation? | p. 1 |
| The Growth in Experimental Design | p. 2 |
| The Six Steps of Experimental Design | p. 3 |
| Plan the Experiment | p. 3 |
| Design the Experiment | p. 5 |
| Perform the Experiment | p. 7 |
| Analyze the Data from the Experiment | p. 7 |
| Confirm the Results of the Experiment | p. 8 |
| Evaluate the Conclusions of the Experiment | p. 8 |
| Experimental-Design Applications in Management | p. 9 |
| Corporate Environmental Behavior | p. 9 |
| Supermarket Decision Variables | p. 11 |
| Financial Services Menu | p. 12 |
| The Qualities of a Superior Motel | p. 13 |
| Time and Ease of Seatbelt Use: A Public Sector Example | p. 15 |
| Emergency Assistance Service for Travelers | p. 15 |
| Perspective | p. 17 |
| Primary Focus on Factors Under Study | p. 19 |
| One-Factor Designs and the Analysis of Variance | p. 21 |
| One-Factor Designs | p. 22 |
| The Statistical Model | p. 25 |
| Estimation of the Parameters of the Model | p. 26 |
| Sums of Squares | p. 28 |
| Analysis of (the) Variance (ANOVA) | p. 31 |
| Forming the F Statistic: Logic and Derivation | p. 35 |
| The Key Fifth Column of the ANOVA Table | p. 35 |
| A Comment | p. 48 |
| Exercises | p. 49 |
| Some Further Issues in One-Factor Designs and ANOVA | p. 59 |
| Introduction | p. 59 |
| Basic Assumptions of ANOVA | p. 59 |
| Kruskal-Wallis Test | p. 63 |
| Review of Hypothesis Testing | p. 66 |
| p Value | p. 70 |
| Type I and Type II Errors | p. 71 |
| Back to ANOVA | p. 73 |
| Power | |
| Power Considerations in Determination of Required Sample Size | p. 76 |
| Confidence Intervals | p. 82 |
| Exercises | p. 84 |
| Multiple-Comparison Testing | p. 87 |
| Logic of Multiple-Comparison Testing | p. 88 |
| Type I Errors in Multiple-Comparison Testing | p. 90 |
| Pairwise Comparisons | p. 93 |
| Fisher's Least Significant Difference Test | p. 93 |
| Tukey's Honestly Significant Difference Test | p. 99 |
| Newman-Keuls Test with Example | p. 103 |
| Two Other Tests Comparing All Pairs of Column Means | p. 105 |
| Dunnett's Test | p. 106 |
| Post Hoc Exploratory Comparisons--The Scheffe Test | p. 108 |
| Carrying Out the Test | p. 109 |
| Discussion of Scheffe Test | p. 111 |
| Exercises | p. 125 |
| Orthogonality, Orthogonal Decomposition, and Their Role in Modern Experimental Design | p. 129 |
| Introduction | p. 130 |
| Forming an Orthogonal Matrix | p. 131 |
| Exercises | p. 151 |
| Two-Factor Cross-Classification Designs | p. 155 |
| Introduction to Studying Two Factors | p. 156 |
| Designs with Replication | p. 157 |
| The Model | p. 159 |
| Parameter Estimates | p. 159 |
| Interaction | p. 160 |
| Back to the Statistical Model: Sums of Squares | p. 164 |
| Fixed Levels versus Random Levels | p. 177 |
| Two Factors with No Replication and No Interaction | p. 182 |
| Blocking | p. 187 |
| Friedman Nonparametric Test | p. 188 |
| Perspective on Friedman Test | p. 191 |
| Exercises | p. 192 |
| Nested, or Hierarchical, Designs | p. 201 |
| Introduction to Nested Designs | p. 202 |
| The Model | p. 204 |
| Discussion | p. 213 |
| Exercises | p. 214 |
| Designs with Three or More Factors: Latin-Square and Related Designs | p. 221 |
| Introduction to Multifactor Designs | p. 222 |
| Latin-Square Designs | p. 223 |
| The Latin-Square Model and ANOVA | p. 227 |
| Graeco-Latin-Square Designs | p. 234 |
| Other Designs with Three or More Factors | p. 237 |
| Exercises | p. 240 |
| Primary Focus on the Number of Levels of a Factor | p. 245 |
| Two-Level Factorial Designs | p. 247 |
| Introduction | p. 248 |
| Two-Factor Experiments | p. 249 |
| Remarks on Effects and Interactions | p. 252 |
| Symbolism, Notation, and Language | p. 252 |
| Table of Signs | p. 253 |
| Modern Notation and Yates' Order | p. 257 |
| Three Factors, Each at Two Levels | p. 258 |
| Estimating Effects in Three-Factor, Two-Level Designs | p. 259 |
| Number and Kinds of Effects | p. 264 |
| Yates' Forward Algorithm | p. 266 |
| A Note on Replicated 2[superscript k] Experiments | p. 268 |
| Main Effects in the Face of Large Interactions | p. 271 |
| Levels of Factors | p. 273 |
| Factorial Designs versus Designs Varying Factors One at a Time | p. 274 |
| Factors Not Studied | p. 277 |
| Errors of Estimates in 2[superscript k] Designs | p. 278 |
| Comment on Testing the Effects in 2[superscript k] Designs | p. 280 |
| Exercises | p. 281 |
| Confounding/Blocking in 2[superscript k] Designs | p. 289 |
| Introduction | p. 290 |
| Simple Confounding | p. 290 |
| Partial Confounding | p. 295 |
| Multiple Confounding | p. 298 |
| Mod-2 Multiplication | p. 299 |
| Determining the Blocks | p. 301 |
| Number of Blocks and Confounded Effects | p. 304 |
| Comment on Calculating Effects | p. 306 |
| Detailed Example of Error Reduction through Confounding | p. 307 |
| Exercises | p. 308 |
| Two-Level Fractional-Factorial Designs | p. 311 |
| Introduction | p. 313 |
| 2[superscript k-p] Designs | p. 314 |
| Yates' Algorithm Revisited | p. 324 |
| Quarter-Replicate Designs | p. 327 |
| Orthogonality Revisited | p. 331 |
| Power and Minimum Detectable Effects in 2[superscript k-p] Designs | p. 345 |
| Selection of a Workable Set of Dead Letters | p. 353 |
| Exercises | p. 354 |
| Designs with Factors at Three Levels | p. 358 |
| Introduction | p. 359 |
| Design with One Factor at Three Levels | p. 359 |
| Design with Two Factors, Each at Three Levels | p. 361 |
| Nonlinearity Recognition and Robustness | p. 371 |
| Three Levels versus Two Levels | p. 372 |
| Unequally Spaced Levels | p. 374 |
| Comment | p. 375 |
| Exercises | p. 375 |
| Introduction to Taguchi Methods | p. 378 |
| Introduction | p. 379 |
| Taguchi's Quality Philosophy and Loss Function | p. 379 |
| Control of the Variability of Performance | p. 382 |
| Taguchi Methods: Designing Fractional-Factorial Designs | p. 384 |
| Experiments without Interactions | p. 386 |
| Experiments with Interactions | p. 388 |
| Taguchi's L[subscript 16] | p. 394 |
| Experiments Involving Nonlinearities or Factors with Three Levels | p. 394 |
| Further Analyses | p. 400 |
| Perspective on Taguchi Methods | p. 406 |
| Exercises | p. 407 |
| Response-Surface Methods, Other Topics, and the Literature of Experimental Design | p. 411 |
| Introduction to Response-Surface Methodology | p. 413 |
| Introduction | p. 413 |
| The Underlying Philosophy of RSM | p. 416 |
| Method of Steepest Ascent | p. 418 |
| Testing the Plane: Center Points | p. 424 |
| Method of Local Exploration | p. 427 |
| Central-Composite Designs | p. 427 |
| Box-Behnken Designs | p. 429 |
| Comparison of Central-Composite and Box-Behnken Designs | p. 430 |
| Issues in the Method of Local Experimentation | p. 431 |
| Perspective on RSM | p. 432 |
| Concluding Remark | p. 451 |
| Exercises | p. 451 |
| Literature on Experimental Design, and Discussion of Some Topics Not Covered in the Text | p. 454 |
| Introduction | p. 454 |
| Literature Discussion | p. 455 |
| Some Classics | p. 455 |
| Recommendations for Specific Topics | p. 456 |
| Discussion of Some Topics Not Covered in the Text | p. 459 |
| Outliers | p. 459 |
| Missing Data | p. 460 |
| Power and Sample Size | p. 460 |
| Time-Series and Failure-Time Experiments | p. 460 |
| Plackett-Burman Designs | p. 461 |
| Repeated-Measures Designs | p. 461 |
| Crossover Designs | p. 462 |
| Mixture Designs | p. 462 |
| Bibliography | p. 463 |
| References | p. 463 |
| Statistical Tables | p. 467 |
| Index | p. 477 |
| Table of Contents provided by Syndetics. All Rights Reserved. |
책소개
Based on decades of teaching, consulting, and industrial experience in the field of design and analysis of experiments, the authors provide an intuitive understanding of the principles of experimental design and analysis. The emphasis is on the application of experimental design concepts in such traditional management and industrial engineering areas such as marketing, operations, management information systems, organizational behavior, and others. The authors also apply this material to such non-profit areas as education, health care, and government. Using popular analytical tools such as SPSS, JMP, and Microsoft Excel, Berger and Maurer emphasize the modern application of experimental design to real problems.
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