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Zusatztext "A good introduction to statistical design of experiments! covering a wide variety of topics in a well readable and structured way." (Statistical Papers 2008)"It is definitely nice to have such a book in the library." (International Statistical Review! 2007)"... this book will prove to be a boon for advances in experimental design." (Zentralblatt MATH! 2007) Informationen zum Autor Ryan is a young author who writes Christian books and articles. Klappentext A complete and well-balanced introduction to modern experimental designUsing current research and discussion of the topic along with clear applications, Modern Experimental Design highlights the guiding role of statistical principles in experimental design construction. This text can serve as both an applied introduction as well as a concise review of the essential types of experimental designs and their applications.Topical coverage includes designs containing one or multiple factors, designs with at least one blocking factor, split-unit designs and their variations as well as supersaturated and Plackett-Burman designs. In addition, the text contains extensive treatment of:* Conditional effects analysis as a proposed general method of analysis* Multiresponse optimization* Space-filling designs, including Latin hypercube and uniform designs* Restricted regions of operability and debarred observations* Analysis of Means (ANOM) used to analyze data from various types of designs* The application of available software, including Design-Expert(r), JMP(r), and MINITAB(r)This text provides thorough coverage of the topic while also introducing the reader to new approaches. Using a large number of references with detailed analyses of datasets, Modern Experimental Design works as a well-rounded learning tool for beginners as well as a valuable resource for practitioners. Zusammenfassung This volume covers the first full year of experimental design topics at the beginning graduate level. It attempts to present a well-balanced, down-to-earth, but complete coverage of the subject matter, with equal emphasis on both design and analysis. The mathematical rigor is strictly kept to a minimum. Inhaltsverzeichnis Preface. 1. Introduction. 1.1 Experiments All Around Us. 1.2 Objectives for Experimental Designs. 1.3 Planned Experimentation versus use of Observational Data. 1.4 Basic Design Concepts. 1.5 Terminology. 1.6 Steps for the Design of Experiments. 1.7 Processes Should Ideally be in a State of Statistical Control. 1.8 Types of Experimental Designs. 1.9 Analysis of Means. 1.10 Missing Data. 1.11 Experimental Designs and Six Sigma. 1.12 Quasi-Experimental Design. 1.13 Summary. 2. Completely Randomized Design. 2.1 completely Randomized Design. 2.2 Analysis of Means. 2.3 Software for Experimental Design. 2.4 Missing Values. 2.5 Summary. 3. Designs that Incorporate Extraneous (Blocking) Factors. 3.1 Randomized Block Design. 3.2 Incomplete Block Designs. Graeco-Latin Square Design. 3.5 Youden Squares. 3.6 Missing Values. 3.7 Software. 3.8 Summary. 4. Full Factorial Designs with Two Levels. 4.1 The nature of Factorial Designs. 4.2 The Deleterious Effects of Interactions. 4.3 Effect Estimates. 4.4 Why Not One-Factor-at-a-Time Designs? 4.5 ANOVA Table for Unreplicated Two-factor Design? 4.6 The 23 Design. 4.7 Built-in Replication. 4.8 Multiple Readings versus Replicates. 4.9 Reality versus Textbook Examples. 4.10 Bad data in Factorial Designs. 4.11 Normal Probability Plot Methods. 4.12 Missing Data in Factorial Designs. 4.13 Inaccurate Levels in Factorial Designs. 4.14 Checking for Statistical Control. 4.15 Blocking 2k Designs. 4.16 The Role of Expected Mean Squares in Experimental Design. 4.17 Hypothesis Tests with Only Random Factors in 2k Designs? Avoid Them! 4.18 Hierarchical versus Nonhierarchical Models. 4.19 Hard-to-Change factors....
