Experiments : Planning, Analysis, and Optimization.

Cover -- Title Page -- Copyright -- Contents -- Preface to the Third Edition -- Preface to the Second Edition -- Preface to the First Edition -- Suggestions of Topics for Instructors -- List of Experiments and Data -- About the Companion Website -- Chapter 1 Basic Concepts for Experimental Design and Introductory Regression Analysis -- 1.1 Introduction and Historical Perspective -- 1.2 A Systematic Approach to the Planning and Implementation of Experiments -- 1.3 Fundamental Principles: Replication, Randomization, and Blocking -- 1.4 Simple Linear Regression -- 1.5 Testing of Hypothesis and Interval Estimation -- 1.6 Multiple Linear Regression -- 1.7 Variable Selection in Regression Analysis -- 1.8 Analysis of Air Pollution Data -- 1.9 Practical Summary -- EXERCISES -- REFERENCES -- Chapter 2 Experiments with a Single Factor -- 2.1 One‐Way Layout -- 2.1.1 Constraint on the Parameters -- 2.2 Multiple Comparisons -- 2.3 Quantitative Factors and Orthogonal Polynomials -- 2.4 Expected Mean Squares and Sample Size Determination -- 2.5 One‐Way Random Effects Model -- 2.6 Residual Analysis: Assessment of Model Assumptions -- 2.7 Practical Summary -- EXERCISES -- REFERENCES -- Chapter 3 Experiments with More than One Factor -- 3.1 Paired Comparison Designs -- 3.2 Randomized Block Designs -- 3.3 Two‐Way Layout: Factors with Fixed Levels -- 3.3.1 Two Qualitative Factors: A Regression Modeling Approach -- 3.4 Two‐Way Layout: Factors with Random Levels -- 3.5 Multi‐Way Layouts -- 3.6 Latin Square Designs: Two Blocking Variables -- 3.7 Graeco‐Latin Square Designs -- 3.8 Balanced Incomplete Block Designs -- 3.9 Split‐Plot Designs -- 3.10 Analysis of Covariance: Incorporating Auxiliary Information -- 3.11 Transformation of the Response -- 3.12 Practical Summary -- EXERCISES.

APPENDIX 3A: TABLE OF LATIN SQUARES,GRAECO-LATIN SQUARES, AND HYPER-GRAECO-LATINSQUARES -- REFERENCES -- Chapter 4 Full Factorial Experiments at Two Levels -- 4.1 An Epitaxial Layer Growth Experiment -- 4.2 Full Factorial Designs at Two Levels: A General Discussion -- 4.3 Factorial Effects and Plots -- 4.3.1 Main Effects -- 4.3.2 Interaction Effects -- 4.4 Using Regression to Compute Factorial Effects -- 4.5 ANOVA Treatment of Factorial Effects -- 4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity -- 4.7 Comparisons with the "One‐Factor‐at‐a‐Time" Approach -- 4.8 Normal and Half‐Normal Plots for Judging Effect Significance -- 4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates -- 4.10 Nominal‐the‐Best Problem and Quadratic Loss Function -- 4.11 Use of Log Sample Variance for Dispersion Analysis -- 4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment -- 4.13 Test of Variance Homogeneity and Pooled Estimate of Variance -- 4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates -- 4.15 Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks -- 4.16 Practical Summary -- EXERCISES -- APPENDIX 4A: TABLE OF 2k FACTORIAL DESIGNS IN 2qBLOCKS -- REFERENCES -- Chapter 5 Fractional Factorial Experiments at Two Levels -- 5.1 A Leaf Spring Experiment -- 5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria of Resolution and Minimum Aberration -- 5.3 Analysis of Fractional Factorial Experiments -- 5.4 Techniques for Resolving the Ambiguities in Aliased Effects -- 5.4.1 Fold‐Over Technique for Follow‐Up Experiments -- 5.4.2 Optimal Design Approach for Follow‐Up Experiments -- 5.5 Conditional Main Effect (CME) Analysis: A Method to Unravel Aliased Interactions.

5.6 Selection of 2k−p Designs Using Minimum Aberration and Related Criteria -- 5.7 Blocking in Fractional Factorial Designs -- 5.8 Practical Summary -- EXERCISES -- APPENDIX 5A: TABLES OF 2k−p FRACTIONAL FACTORIALDESIGNS -- APPENDIX 5B: TABLES OF 2k−p FRACTIONAL FACTORIALDESIGNS IN 2q BLOCKS -- REFERENCES -- Chapter 6 Full Factorial and Fractional Factorial Experiments at Three Levels -- 6.1 A Seat‐Belt Experiment -- 6.2 Larger‐the‐Better and Smaller‐the‐Better Problems -- 6.2.0 Two‐Step Procedure for Larger‐the‐Better Problems -- 6.2.0 Two‐Step Procedure for Smaller‐the‐Better Problems -- 6.3 3k Full Factorial Designs -- 6.4 3k−p Fractional Factorial Designs -- 6.5 Simple Analysis Methods: Plots and Analysis of Variance -- 6.6 An Alternative Analysis Method -- 6.7 Analysis Strategies for Multiple Responses I: Out‐Of‐Spec Probabilities -- 6.8 Blocking in 3k and 3k−p Designs -- 6.9 Practical Summary -- EXERCISES -- APPENDIX 6A: TABLES OF 3k−p FRACTIONAL FACTORIALDESIGNS -- APPENDIX 6B: TABLES OF 3k−p FRACTIONALFACTORIAL DESIGNS IN 3q BLOCKS -- REFERENCES -- Chapter 7 Other Design and Analysis Techniques for Experiments at More than Two Levels -- 7.1 A Router Bit Experiment Based on a Mixed Two‐Level and Four‐Level Design -- 7.2 Method of Replacement and Construction of 2m4n Designs -- 7.3 Minimum Aberration 2m4n Designs with n &amp -- equals -- 1, 2 -- 7.4 An Analysis Strategy for 2m4n Experiments -- 7.5 Analysis of the Router Bit Experiment -- 7.6 A Paint Experiment Based on a Mixed Two‐Level and Three‐Level Design -- 7.7 Design and Analysis of 36‐Run Experiments at Two And Three Levels -- 7.8 rk−p Fractional Factorial Designs for any Prime Number r -- 7.8.1 25‐Run Fractional Factorial Designs at Five Levels -- 7.8.2 49‐Run Fractional Factorial Designs at Seven Levels -- 7.8.3 General Construction -- 7.9 Definitive Screening Designs.

7.10 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling -- 7.10.1 Nested Effects Modeling -- 7.10.2 Analysis of Light Bulb Experiment -- 7.10.3 Response Surface Modeling -- 7.10.4 Symmetric and Asymmetric Relationships Between Related Factors -- 7.11 Practical Summary -- EXERCISES -- APPENDIX 7A: TABLES OF 2m41 MINIMUM ABERRATIONDESIGNS -- APPENDIX 7B: TABLES OF 2m42 MINIMUM ABERRATIONDESIGNS -- APPENDIX 7C: OA(25, 56) -- APPENDIX 7D: OA(49, 78) -- APPENDIX 7E: CONFERENCE MATRICES C6, C8, C10, C12,C14, AND C16 -- REFERENCES -- Chapter 8 Nonregular Designs: Construction and Properties -- 8.1 Two Experiments: Weld‐Repaired Castings and Blood Glucose Testing -- 8.2 Some Advantages of Nonregular Designs Over the 2k−p AND 3k−p Series of Designs -- 8.3 A Lemma on Orthogonal Arrays -- 8.4 Plackett-Burman Designs and Hall's Designs -- 8.5 A Collection of Useful Mixed‐Level Orthogonal Arrays -- 8.6 Construction of Mixed‐Level Orthogonal Arrays Based on Difference Matrices -- 8.6.1 General Method for Constructing Asymmetrical Orthogonal Arrays -- 8.7 Construction of Mixed‐Level Orthogonal Arrays Through the Method of Replacement -- 8.8 Orthogonal Main‐Effect Plans Through Collapsing Factors -- 8.9 Practical Summary -- EXERCISES -- APPENDIX 8A: PLACKETT-BURMAN DESIGNS OA(N, 2N−1)WITH 12 ≤ N ≤ 48 AND N = 4 k BUT NOT A POWEROF 2 -- APPENDIX 8B: HALL'S 16-RUN ORTHOGONAL ARRAYSOF TYPES II TO V -- APPENDIX 8C: SOME USEFUL MIXED-LEVELORTHOGONAL ARRAYS -- APPENDIX 8D: SOME USEFUL DIFFERENCE MATRICES -- APPENDIX 8E: SOME USEFUL ORTHOGONALMAIN-EFFECT PLANS -- REFERENCES -- Chapter 9 Experiments with Complex Aliasing -- 9.1 Partial Aliasing of Effects and the Alias Matrix -- 9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis -- 9.3 Simplification of Complex Aliasing via Effect Sparsity.

9.4 An Analysis Strategy for Designs with Complex Aliasing -- 9.4.1 Some Limitations -- 9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing -- 9.5.1 Bayesian Model Priors -- 9.5.2 Gibbs Sampling -- 9.5.3 Choice of Prior Tuning Constants -- 9.5.4 Blood Glucose Experiment Revisited -- 9.5.5 Other Applications -- 9.6 Supersaturated Designs: Design Construction and Analysis -- 9.7 Practical Summary -- EXERCISES -- APPENDIX 9A: FURTHER DETAILS FOR THE FULLCONDITIONAL DISTRIBUTIONS -- REFERENCES -- Chapter 10 Response Surface Methodology -- 10.1 A Ranitidine Separation Experiment -- 10.2 Sequential Nature of Response Surface Methodology -- 10.3 From First‐Order Experiments to Second‐Order Experiments: Steepest Ascent Search and Rectangular Grid Search -- 10.3.1 Curvature Check -- 10.3.2 Steepest Ascent Search -- 10.3.3 Rectangular Grid Search -- 10.4 Analysis of Second‐Order Response Surfaces -- 10.4.1 Ridge Systems -- 10.5 Analysis of the Ranitidine Experiment -- 10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions -- 10.7 Central Composite Designs -- 10.8 Box-Behnken Designs and Uniform Shell Designs -- 10.9 Practical Summary -- EXERCISES -- APPENDIX 10A: TABLE OF CENTRAL COMPOSITEDESIGNS -- APPENDIX 10B: TABLE OF BOX-BEHNKEN DESIGNS -- APPENDIX 10C: TABLE OF UNIFORM SHELL DESIGNS -- REFERENCES -- Chapter 11 Introduction to Robust Parameter Design -- 11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments -- 11.1.1 Layer Growth Experiment Revisited -- 11.1.2 Leaf Spring Experiment Revisited -- 11.2 Strategies for Reducing Variation -- 11.3 Noise (Hard‐to‐Control) Factors -- 11.4 Variation Reduction Through Robust Parameter Design -- 11.5 Experimentation and Modeling Strategies I: Cross Array -- 11.5.1 Location and Dispersion Modeling.

11.5.2 Response Modeling.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.