{"product_id":"modern-experimental-design-hardback-9780471210771","title":"Modern Experimental Design (Hardback) 9780471210771","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModern Experimental Design\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eThomas P. Ryan (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471210771, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 16 February 2007\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e624 pages, Charts: 1 B\u0026amp;W, 0 Color; Tables: 0 B\u0026amp;W, 0 Color; Graphs: 48 B\u0026amp;W, 0 Color\u003cbr\u003e24.4 x 16.3 x 3.6 cm, 1.007 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"A good introduction to statistical design of experiments, covering a wide variety of topics in a  well readable and structured way.\" (\u003ci\u003eStatistical Papers\u003c\/i\u003e 2008)  \u003cp\u003e\"Modern Experimental Design is a must-have reference for anyone who will be designing experiments or for statisticians interested in remaining on the leading edge of this important area.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e)\u003c\/p\u003e \u003cp\u003e\"This attractive text is written in a precise style that interconnects and builds on discussion, examples, and methods from chapter to chapter. Especially pleasant are the care and attention devoted to details. The comprehensive and easy-to-read style of writing suggests that statistics is fun and exploratory.\" (\u003ci\u003eComputing Reviews\u003c\/i\u003e, 2008)\u003c\/p\u003e \u003cp\u003e\"... this book will prove to be a boon for advances in experimental design.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2007)\u003c\/p\u003e \u003cp\u003e\"…the author's wealth of knowledge is immediately evident…an excellent expose concerning the actual statistical planning or 'design' of experiments.\" (\u003ci\u003eBiometrics\u003c\/i\u003e, September 2007)\u003c\/p\u003e \u003cp\u003e\"A very interesting and useful book…highly recommended.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, August 2007)\u003c\/p\u003e \u003cp\u003e\"It is definitely nice to have such a book in the library.\" (\u003ci\u003eInternational Statistical Review, 2007)\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eA complete and well-balanced introduction to modern experimental design\u003c\/b\u003e  \u003cp\u003eUsing current research and discussion of the topic along with clear applications, \u003ci\u003eModern Experimental Design\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eTopical 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eConditional effects analysis as a proposed general method of analysis\u003c\/li\u003e \u003cli\u003eMultiresponse optimization\u003c\/li\u003e \u003cli\u003eSpace-filling designs, including Latin hypercube and uniform designs\u003c\/li\u003e \u003cli\u003eRestricted regions of operability and debarred observations\u003c\/li\u003e \u003cli\u003eAnalysis of Means (ANOM) used to analyze data from various types of designs\u003c\/li\u003e \u003cli\u003eThe application of available software, including Design-Expert, JMP, and MINITAB\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis 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, \u003ci\u003eModern Experimental Design\u003c\/i\u003e works as a well-rounded learning tool for beginners as well as a valuable resource for practitioners.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e1. Introduction.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 1.1 Experiments All Around Us.  \u003cp\u003e\u003c\/p\u003e 1.2 Objectives for Experimental Designs.  \u003cp\u003e\u003c\/p\u003e 1.3 Planned Experimentation versus use of Observational Data.  \u003cp\u003e\u003c\/p\u003e 1.4 Basic Design Concepts.  \u003cp\u003e\u003c\/p\u003e 1.5 Terminology.  \u003cp\u003e\u003c\/p\u003e 1.6 Steps for the Design of Experiments.  \u003cp\u003e\u003c\/p\u003e 1.7 Processes Should Ideally be in a State of \u003cst1:state w:st=\"on\"\u003e\u003cst1:place w:st=\"on\"\u003eStatistical Control\u003c\/st1:place\u003e\u003c\/st1:state\u003e.  \u003cp\u003e\u003c\/p\u003e 1.8 Types of Experimental Designs.  \u003cp\u003e\u003c\/p\u003e 1.9 Analysis of Means.  \u003cp\u003e\u003c\/p\u003e 1.10 Missing Data.  \u003cp\u003e\u003c\/p\u003e 1.11 Experimental Designs and Six Sigma.  \u003cp\u003e\u003c\/p\u003e 1.12 Quasi-Experimental Design.  \u003cp\u003e\u003c\/p\u003e 1.13 Summary.  \u003cp\u003e\u003c\/p\u003e  \u003cb\u003e2. Completely Randomized Design.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 2.1 completely Randomized Design.  \u003cp\u003e\u003c\/p\u003e 2.2 Analysis of Means.  \u003cp\u003e\u003c\/p\u003e 2.3 Software for Experimental Design.  \u003cp\u003e\u003c\/p\u003e 2.4 Missing Values.  \u003cp\u003e\u003c\/p\u003e 2.5 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e3. Designs that Incorporate Extraneous (Blocking) Factors.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 3.1 Randomized Block Design.  \u003cp\u003e\u003c\/p\u003e 3.2 Incomplete Block Designs.  \u003cp\u003e\u003c\/p\u003e \u003clatin\u003e\u003c\/latin\u003eGraeco-Latin Square Design.  \u003cp\u003e\u003c\/p\u003e 3.5 Youden Squares.  \u003cp\u003e\u003c\/p\u003e 3.6 Missing Values.  \u003cp\u003e\u003c\/p\u003e 3.7 Software.  \u003cp\u003e\u003c\/p\u003e 3.8 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e4. Full Factorial Designs with Two Levels.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 4.1 The nature of Factorial Designs.  \u003cp\u003e\u003c\/p\u003e 4.2 The Deleterious Effects of Interactions.  \u003cp\u003e\u003c\/p\u003e 4.3 Effect Estimates.  \u003cp\u003e\u003c\/p\u003e 4.4 Why Not One-Factor-at-a-Time Designs?  \u003cp\u003e\u003c\/p\u003e 4.5 ANOVA Table for Unreplicated Two-factor Design?  \u003cp\u003e\u003c\/p\u003e 4.6 The 2\u003csup\u003e3\u003c\/sup\u003e Design.  \u003cp\u003e\u003c\/p\u003e 4.7 Built-in Replication.  \u003cp\u003e\u003c\/p\u003e 4.8 Multiple Readings versus Replicates.  \u003cp\u003e\u003c\/p\u003e 4.9 Reality versus Textbook Examples.  \u003cp\u003e\u003c\/p\u003e 4.10 Bad data in Factorial Designs.  \u003cp\u003e\u003c\/p\u003e 4.11 Normal Probability Plot Methods.  \u003cp\u003e\u003c\/p\u003e 4.12 Missing Data in Factorial Designs.  \u003cp\u003e\u003c\/p\u003e 4.13 Inaccurate Levels in Factorial Designs.  \u003cp\u003e\u003c\/p\u003e 4.14 Checking for Statistical Control.  \u003cp\u003e\u003c\/p\u003e 4.15 Blocking 2\u003csup\u003ek\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 4.16 The Role of Expected Mean Squares in Experimental Design.  \u003cp\u003e\u003c\/p\u003e 4.17 Hypothesis Tests with Only Random Factors in 2\u003csup\u003ek\u003c\/sup\u003e Designs? Avoid Them!  \u003cp\u003e\u003c\/p\u003e 4.18 Hierarchical versus Nonhierarchical Models.  \u003cp\u003e\u003c\/p\u003e 4.19 Hard-to-Change factors.  \u003cp\u003e\u003c\/p\u003e 4.20 Factors Not reset.  \u003cp\u003e\u003c\/p\u003e 4.21 Detecting Dispersion Effects.  \u003cp\u003e\u003c\/p\u003e 4.22 Software.  \u003cp\u003e\u003c\/p\u003e 4.23 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e5. Fractional factorial Designs with Two-Levels.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 5.1 2\u003csup\u003ek-1\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 5.2 2\u003csup\u003ek-2\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 5.3 Designs with k \u0026amp; p = 16.  \u003cp\u003e\u003c\/p\u003e 5.4 Utility of Small Fractional factorials vis-à-vis Normal Probability Plots.  \u003cp\u003e\u003c\/p\u003e 5.5 Design Efficiency.  \u003cp\u003e\u003c\/p\u003e 5.6 Retrieving a Lost Defining Relation.  \u003cp\u003e\u003c\/p\u003e 5.7 Minimum Aberration Designs and Minimum Confounded Effects Designs.  \u003cp\u003e\u003c\/p\u003e 5.8 Blocking Factorial Design.  \u003cp\u003e\u003c\/p\u003e 5.9 Foldover Designs.  \u003cp\u003e\u003c\/p\u003e 5.10 John’s ¾ Designs.  \u003cp\u003e\u003c\/p\u003e 5.11 Projective Properties of 2\u003csup\u003ek-p\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 5.12 Small Fractions and Irregular Designs.  \u003cp\u003e\u003c\/p\u003e 5.13 An Example of Sequential Experimentation.  \u003cp\u003e\u003c\/p\u003e 5.14 Inadvertent Nonorthogonality—Case Study.  \u003cp\u003e\u003c\/p\u003e 5.15 Fractional factorial Designs for Natural Subsets of factors.  \u003cp\u003e\u003c\/p\u003e 5.16 Relationship Between Fractional Factorials and Latin Squares.  \u003cp\u003e\u003c\/p\u003e 5.17 Alternatives to Fractional Factorials.  \u003cp\u003e\u003c\/p\u003e 5.18 Missing and Bad data.  \u003cp\u003e\u003c\/p\u003e 5.19 Plackett-Burman Designs.  \u003cp\u003e\u003c\/p\u003e 5.20 Software.  \u003cp\u003e\u003c\/p\u003e 5.21 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e6. Designs With More Than Two Levels.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 6.1 3\u003csup\u003ek\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 6.2 Conditional Effects.  \u003cp\u003e\u003c\/p\u003e 6.3 3\u003csup\u003ek-p\u003c\/sup\u003e Designs.  \u003cp\u003e\u003c\/p\u003e 6.4 Mixed factorials.  \u003cp\u003e\u003c\/p\u003e 6.5 Mixed Fractional Factorials.  \u003cp\u003e\u003c\/p\u003e 6.6 Orthogonal Arrays with Mixed levels.  \u003cp\u003e\u003c\/p\u003e 6.7 Minimum Aberration Designs and Minimum Confounded Effects Designs.  \u003cp\u003e\u003c\/p\u003e 6.8 Four or More Levels.  \u003cp\u003e\u003c\/p\u003e 6.9 Software.  \u003cp\u003e\u003c\/p\u003e 6.10 Catalog of Designs.  \u003cp\u003e\u003c\/p\u003e 6.11 Summary.  \u003cp\u003e\u003c\/p\u003e 7. Nested Designs.  \u003cp\u003e\u003c\/p\u003e 7.1 Various Examples.  \u003cp\u003e\u003c\/p\u003e 7.2 Software Shortcomings.  \u003cp\u003e\u003c\/p\u003e 7.3 Staggered Nested Designs.  \u003cp\u003e\u003c\/p\u003e 7.4 Nested and Staggered Nested Designs with factorial Structure.  \u003cp\u003e\u003c\/p\u003e 7.5 Estimating Variance Components.  \u003cp\u003e\u003c\/p\u003e 7.6 ANOM for Nested Designs?  \u003cp\u003e\u003c\/p\u003e 7.7 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e8. Robust Designs.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 8.1 “Taguchi Designs?”  \u003cp\u003e\u003c\/p\u003e 8.2 Identification of Dispersion Effects.  \u003cp\u003e\u003c\/p\u003e 8.3 Designs with Noise factors.  \u003cp\u003e\u003c\/p\u003e 8.4 Product Array, Combined Array, or Compound Array?  \u003cp\u003e\u003c\/p\u003e 8.5 Software.  \u003cp\u003e\u003c\/p\u003e 8.6 Further \u003cst1:city w:st=\"on\"\u003e\u003cst1:place w:st=\"on\"\u003eReading\u003c\/st1:place\u003e\u003c\/st1:city\u003e.  \u003cp\u003e\u003c\/p\u003e 8.7 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e9. Split-Unit, Split-Lot, and Related Designs.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 9.1 \u003cst1:city w:st=\"on\"\u003e\u003cst1:place w:st=\"on\"\u003eSplit\u003c\/st1:place\u003e\u003c\/st1:city\u003e-Unit Design.  \u003cp\u003e\u003c\/p\u003e 9.2 \u003cst1:city w:st=\"on\"\u003eSplit\u003c\/st1:city\u003e-\u003cst1:place w:st=\"on\"\u003eLot\u003c\/st1:place\u003e Design.  \u003cp\u003e\u003c\/p\u003e 9.3 Commonalities and Differences Between these Designs.  \u003cp\u003e\u003c\/p\u003e 9.4 Software.  \u003cp\u003e\u003c\/p\u003e 9.5 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e10. Response Surface Designs.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 10.1 Response Surface Experimentation: One Design or More Than One?  \u003cp\u003e\u003c\/p\u003e 10.2 Which Designs?  \u003cp\u003e\u003c\/p\u003e 10.3 Classical Response Surface Designs versus Alternatives.  \u003cp\u003e\u003c\/p\u003e 10.4 Methods of Steepest Ascent (Descent).  \u003cp\u003e\u003c\/p\u003e 10.5 Central Composite Designs.  \u003cp\u003e\u003c\/p\u003e 10.6 Properties of Space-Filling Designs.  \u003cp\u003e\u003c\/p\u003e 10.7 Applications of Uniform Designs.  \u003cp\u003e\u003c\/p\u003e 10.8 Box-Behnken Designs.  \u003cp\u003e\u003c\/p\u003e 10.9 Conditional Effects?  \u003cp\u003e\u003c\/p\u003e 10.10 Other Response Surface Designs.  \u003cp\u003e\u003c\/p\u003e 10.11 Blocking Response Surface Designs.  \u003cp\u003e\u003c\/p\u003e 10.12 Comparison of Designs.  \u003cp\u003e\u003c\/p\u003e 10.13 Analyzing the Fitted Surface.  \u003cp\u003e\u003c\/p\u003e 10.14 Response Surface Designs for Computer Simulations.  \u003cp\u003e\u003c\/p\u003e 10.15 ANOM with Response Surface Designs?  \u003cp\u003e\u003c\/p\u003e 10.16 Further reading.  \u003cp\u003e\u003c\/p\u003e 10.17 The Present and Future Direction of response Surface Designs.  \u003cp\u003e\u003c\/p\u003e 10.18 Software.  \u003cp\u003e\u003c\/p\u003e 10.19 Catalogs of Designs.  \u003cp\u003e\u003c\/p\u003e 10.20 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e11. Repeated Measure Designs.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e   11.1 One factor.  \u003cp\u003e\u003c\/p\u003e 11.2 More Than One Factor.  \u003cp\u003e\u003c\/p\u003e 11.3 Crossover Designs.  \u003cp\u003e\u003c\/p\u003e 11.4 Designs for Carryover Effects.  \u003cp\u003e\u003c\/p\u003e 11.5 How Many Repeated Measures?  \u003cp\u003e\u003c\/p\u003e 11.6 Further \u003cst1:city w:st=\"on\"\u003e\u003cst1:place w:st=\"on\"\u003eReading\u003c\/st1:place\u003e\u003c\/st1:city\u003e.  \u003cp\u003e\u003c\/p\u003e 11.7 Software.  \u003cp\u003e\u003c\/p\u003e 11.8 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e12. Multiple Responses.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 12.1 Overlaying Contour Plots.  \u003cp\u003e\u003c\/p\u003e 12.2 Seeking Multiple Response Optimization with Desirability Functions.  \u003cp\u003e\u003c\/p\u003e 12.3 Dual response Optimization.  \u003cp\u003e\u003c\/p\u003e   12.4 Designs Used with Multiple Responses.  \u003cp\u003e\u003c\/p\u003e 12.5 Applications.  \u003cp\u003e\u003c\/p\u003e 12.6 Multiple Response Optimization variations.  \u003cp\u003e\u003c\/p\u003e 12.7 The Importance of Analysis.  \u003cp\u003e\u003c\/p\u003e 12.8 Software.  \u003cp\u003e\u003c\/p\u003e 12.9 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e13. Miscellaneous Design Topics.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 13.1 One-Factor-at-a-Time Designs.  \u003cp\u003e\u003c\/p\u003e 13.2 Cotter Designs.  \u003cp\u003e\u003c\/p\u003e 13.3 Rotation Designs.  \u003cp\u003e\u003c\/p\u003e 13.4 Screening Designs.  \u003cp\u003e\u003c\/p\u003e 13.5 Design of Experiments for Analytic Studies.  \u003cp\u003e\u003c\/p\u003e 13.6 Equileverage Designs.  \u003cp\u003e\u003c\/p\u003e 13.7 Optimal Designs.  \u003cp\u003e\u003c\/p\u003e 13.8 Designs for Restricted Regions of Operability.  \u003cp\u003e\u003c\/p\u003e 13.9 Space-Filling, Designs.  \u003cp\u003e\u003c\/p\u003e 13.10 Trend-Free Designs.  \u003cp\u003e\u003c\/p\u003e 13.11 Cost-Minimizing Designs.  \u003cp\u003e\u003c\/p\u003e 13.12 Mixture Designs.  \u003cp\u003e\u003c\/p\u003e 13.13 Design of Measurement Capability Studies.  \u003cp\u003e\u003c\/p\u003e 13.14 Design of Computer Experiments.  \u003cp\u003e\u003c\/p\u003e 13.15 Design of Experiments for Categorical Response variables.  \u003cp\u003e\u003c\/p\u003e 13.16 Weighing Designs and Calibration Designs.  \u003cp\u003e\u003c\/p\u003e 13.17 Designs for Assessing the Capability of a System.  \u003cp\u003e\u003c\/p\u003e 13.18 Designs for Nonlinear Models.  \u003cp\u003e\u003c\/p\u003e 13.19 Model-Robust Designs.  \u003cp\u003e\u003c\/p\u003e 13.20 Designs and Analyses for Non-normal Responses.  \u003cp\u003e\u003c\/p\u003e 13.21 Design of Microarray Experiments.  \u003cp\u003e\u003c\/p\u003e 13.22 Multi-Vari Plot.  \u003cp\u003e\u003c\/p\u003e 13.23 Evolutionary Operation.  \u003cp\u003e\u003c\/p\u003e 13.24 Software.  \u003cp\u003e\u003c\/p\u003e 13.25 Summary.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e14. Tying It All Together.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 14.1 Training for Experimental Design Use.  \u003cp\u003e\u003c\/p\u003e Answers to Selected Exercises.  \u003cp\u003e\u003c\/p\u003e Appendix: Statistical Tables.  \u003cp\u003e\u003c\/p\u003e Author Inde.  \u003cp\u003e\u003c\/p\u003e Subject Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52286309138712,"sku":"9780471210771","price":108.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471210771.jpg?v=1781549539","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/modern-experimental-design-hardback-9780471210771","provider":"Freshly Printed Books","version":"1.0","type":"link"}