LEADER 05679nam 2200709 a 450 001 9910143691403321 005 20170815110544.0 010 $a1-282-24228-8 010 $a9786613813404 010 $a0-470-07276-8 010 $a0-470-07275-X 035 $a(CKB)1000000000356766 035 $a(EBL)291421 035 $a(OCoLC)137337570 035 $a(SSID)ssj0000236845 035 $a(PQKBManifestationID)11240144 035 $a(PQKBTitleCode)TC0000236845 035 $a(PQKBWorkID)10173425 035 $a(PQKB)11688033 035 $a(MiAaPQ)EBC291421 035 $a(EXLCZ)991000000000356766 100 $a20060324d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aResponse surfaces, mixtures, and ridge analyses$b[electronic resource] /$fGeorge E.P. Box, Norman R. Draper 205 $a2nd ed. 210 $aHoboken, N.J. $cJohn Wiley$dc2007 215 $a1 online resource (873 p.) 225 1 $aWiley Series in Probability and Statistics ;$vv.649 300 $aDescription based upon print version of record. 311 $a0-470-05357-7 320 $aIncludes bibliographical references (p. 757-850) and index. 327 $aResponse Surfaces, Mixtures, and Ridge Analyses; Contents; Preface to the Second Edition; 1. Introduction to Response Surface Methodology; 1.1. Response Surface Methodology (RSM); 1.2. Indeterminancy of Experimentation; 1.3. Iterative Nature of the Experimental Learning Process; 1.4. Some Classes of Problems (Which, How, Why); 1.5. Need for Experimental Design; 1.6. Geometric Representation of Response Relationships; 1.7. Three Kinds of Applications; 2. The Use Of Graduating Functions; 2.1. Approximating Response Functions; 2.2. An Example; Appendix 2A. A Theoretical Response Function 327 $a3. Least Squares for Response Surface Work3.1. The Method of Least Squares; 3.2. Linear Models; 3.3. Matrix Formulas for Least Squares; 3.4. Geometry of Least Squares; 3.5. Analysis of Variance for One Regressor; 3.6. Least Squares for Two Regressors; 3.7. Geometry of the Analysis of Variance for Two Regressors; 3.8. Orthogonalizing the Second Regressor, Extra Sum of Squares Principle; 3.9. Generalization to p Regressors; 3.10. Bias in Least-Squares Estimators Arising from an Inadequate Model; 3.11. Pure Error and Lack of Fit; 3.12. Confidence Intervals and Confidence Regions 327 $a3.13. Robust Estimation, Maximum Likelihood, and Least SquaresAppendix 3A. Iteratively Reweighted Least Squares; Appendix 3B. Justification of Least Squares by the Gauss-Markov Theorem; Robustness; Appendix 3C. Matrix Theory; Appendix 3D. Nonlinear Estimation; Appendix 3E. Results Involving V(y); Exercises; 4. Factorial Designs at Two Levels; 4.1. The Value of Factorial Designs; 4.2. Two-Level Factorials; 4.3. A 2(6) Design Used in a Study of Dyestuffs Manufacture; 4.4. Diagnostic Checking of the Fitted Models, 2(6) Dyestuffs Example; 4.5. Response Surface Analysis of the 2(6) Design Data 327 $aAppendix 4A. Yates' Method for Obtaining the Factorial Effects for a Two-Level DesignAppendix 4B. Normal Plots on Probability Paper; Appendix 4C. Confidence Regions for Contour Planes (see Section 4.5); Exercises; 5. Blocking and Fractionating 2(k) Factorial Designs; 5.1. Blocking the 2(6) Design; 5.2. Fractionating the 2(6) Design; 5.3. Resolution of a 2(k-p) Factorial Design; 5.4. Construction of 2(k-p) Designs of Resolution III and IV; 5.5. Combination of Designs from the Same Family; 5.6. Screening, Using 2(k-p) Designs (Involving Projections to Lower Dimensions) 327 $a5.7. Complete Factorials Within Fractional Factorial Designs5.8. Plackett and Burman Designs for n = 12 to 60 (but not 52); 5.9. Screening, Using Plackett and Burman Designs (Involving Projections to Lower Dimensions); 5.10. Efficient Estimation of Main Effects and Two-Factor Interactions Using Relatively Small Two-Level Designs; 5.11. Designs of Resolution V and of Higher Resolution; 5.12. Application of Fractional Factorial Designs to Response Surface Methodology; 5.13. Plotting Effects from Fractional Factorials on Probability Paper; Exercises 327 $a6. The Use of Steepest Ascent to Achieve Process Improvement 330 $aThe authority on building empirical models and the fitting of such surfaces to data-completely updated and revised Revising and updating a volume that represents the essential source on building empirical models, George Box and Norman Draper-renowned authorities in this field-continue to set the standard with the Second Edition of Response Surfaces, Mixtures, and Ridge Analyses, providing timely new techniques, new exercises, and expanded material. A comprehensive introduction to building empirical models, this book presents the general philosophy and computational details of a number o 410 0$aWiley Series in Probability and Statistics 606 $aExperimental design 606 $aResponse surfaces (Statistics) 606 $aMixture distributions (Probability theory) 606 $aRidge regression (Statistics) 608 $aElectronic books. 615 0$aExperimental design. 615 0$aResponse surfaces (Statistics) 615 0$aMixture distributions (Probability theory) 615 0$aRidge regression (Statistics) 676 $a519.57 700 $aBox$b George E. P$030397 701 $aDraper$b Norman Richard$0101833 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143691403321 996 $aResponse surfaces, mixtures, and ridge analyses$92149292 997 $aUNINA