{"product_id":"experiments-planning-analysis-and-optimization-planning-analysis-and-parameter-design-optimization-wiley-series-in-probability-and-statistics-552-9780471699460","title":"Experiments Planning Analysis and Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExperimentation is one of the most common activities in which all people engage. In this thoroughly updated Second Edition,  Experiments  presents the most modern, up-to-date treatment in the design and analysis of experiment topics currently available.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“On the whole, I think the book is ideal for a year-long course at the graduate level (there is much more material in the book than can be reasonably covered even in a year-long course), but is still advanced for  undergraduates.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface to the Second Edition.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface to the First Edition.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSuggestions of Topics for Instructors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Experiments and Data Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Basic Concepts for Experimental Design and Introductory Regression Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction and Historical Perspective.\u003c\/p\u003e \u003cp\u003e1.2 A Systematic Approach to the Planning and Implementation of Experiments.\u003c\/p\u003e \u003cp\u003e1.3 Fundamental Principles: Replication, Randomization, and Blocking.\u003c\/p\u003e \u003cp\u003e1.4 Simple Linear Regression.\u003c\/p\u003e \u003cp\u003e1.5 Testing of Hypothesis and Interval Estimation.\u003c\/p\u003e \u003cp\u003e1.6 Multiple Linear Regression.\u003c\/p\u003e \u003cp\u003e1.7 Variable Selection in Regression Analysis.\u003c\/p\u003e \u003cp\u003e1.8 Analysis of Air Pollution Data.\u003c\/p\u003e \u003cp\u003e1.9 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Experiments with a Single Factor.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 One-Way Layout.\u003c\/p\u003e \u003cp\u003e2.2 Multiple Comparisons.\u003c\/p\u003e \u003cp\u003e2.3 Quantitative Factors and Orthogonal Polynomials.\u003c\/p\u003e \u003cp\u003e2.4 Expected Mean Squares and Sample Size Determination.\u003c\/p\u003e \u003cp\u003e2.5 One-Way Random Effects Model.\u003c\/p\u003e \u003cp\u003e2.6 Residual Analysis: Assessment of Model Assumptions.\u003c\/p\u003e \u003cp\u003e2.7 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Experiments with More Than One Factor.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Paired Comparison Designs.\u003c\/p\u003e \u003cp\u003e3.2 Randomized Block Designs.\u003c\/p\u003e \u003cp\u003e3.3 Two-Way Layout: Factors With Fixed Levels.\u003c\/p\u003e \u003cp\u003e3.4 Two-Way Layout: Factors With Random Levels.\u003c\/p\u003e \u003cp\u003e3.5 Multi-Way Layouts.\u003c\/p\u003e \u003cp\u003e3.6 Latin Square Designs: Two Blocking Variables.\u003c\/p\u003e \u003cp\u003e3.7 Graeco-Latin Square Designs.\u003c\/p\u003e \u003cp\u003e3.8 Balanced Incomplete Block Designs.\u003c\/p\u003e \u003cp\u003e3.9 Split-Plot Designs.\u003c\/p\u003e \u003cp\u003e3.10 Analysis of Covariance: Incorporating Auxiliary Information.\u003c\/p\u003e \u003cp\u003e3.11 Transformation of the Response.\u003c\/p\u003e \u003cp\u003e3.12 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Full Factorial Experiments at Two Levels.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 An Epitaxial Layer Growth Experiment.\u003c\/p\u003e \u003cp\u003e4.2 Full Factorial Designs at Two Levels: A General Discussion.\u003c\/p\u003e \u003cp\u003e4.3 Factorial Effects and Plots.\u003c\/p\u003e \u003cp\u003e4.4 Using Regression to Compute Factorial Effects.\u003c\/p\u003e \u003cp\u003e4.5 ANOVA Treatment of Factorial Effects.\u003c\/p\u003e \u003cp\u003e4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity.\u003c\/p\u003e \u003cp\u003e4.7 Comparisons with the \"One-Factor-at-a-Time\" Approach.\u003c\/p\u003e \u003cp\u003e4.8 Normal and Half-Normal Plots for Judging Effect Significance.\u003c\/p\u003e \u003cp\u003e4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates.\u003c\/p\u003e \u003cp\u003e4.10 Nominal-the-Best Problem and Quadratic Loss Function.\u003c\/p\u003e \u003cp\u003e4.11 Use of Log Sample Variance for Dispersion Analysis.\u003c\/p\u003e \u003cp\u003e4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment.\u003c\/p\u003e \u003cp\u003e4.13 Test of Variance Homogeneity and Pooled Estimate of Variance.\u003c\/p\u003e \u003cp\u003e4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments with Variance Estimates.\u003c\/p\u003e \u003cp\u003e4.15 Blocking and Optimal Arrangement of 2\u003csup\u003ek\u003c\/sup\u003e Factorial Designs in 2\u003csup\u003eq\u003c\/sup\u003e Blocks.\u003c\/p\u003e \u003cp\u003e4.16 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fractional Factorial Experiments at Two Levels.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 A Leaf Spring Experiment.\u003c\/p\u003e \u003cp\u003e5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration.\u003c\/p\u003e \u003cp\u003e5.3 Analysis of Fractional Factorial Experiments.\u003c\/p\u003e \u003cp\u003e5.4 Techniques for Resolving the Ambiguities in Aliased Effects.\u003c\/p\u003e \u003cp\u003e5.5 Selection of 2\u003csup\u003ek-p\u003c\/sup\u003e Designs Using Minimum Aberration and Related Criteria.\u003c\/p\u003e \u003cp\u003e5.6 Blocking in Fractional Factorial Designs.\u003c\/p\u003e \u003cp\u003e5.7 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Full Factorial and Fractional Factorial Experiments at Three \u003c\/b\u003e \u003cb\u003eLevels.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 A Seat-Belt Experiment.\u003c\/p\u003e \u003cp\u003e6.2 Larger-the-Better and Smaller-the-Better Problems.\u003c\/p\u003e \u003cp\u003e6.3 3\u003csup\u003ek\u003c\/sup\u003e Full Factorial Designs.\u003c\/p\u003e \u003cp\u003e6.4 3\u003csup\u003ek-p\u003c\/sup\u003e Fractional Factorial Designs.\u003c\/p\u003e \u003cp\u003e6.5 Simple Analysis Methods: Plots and Analysis of Variance.\u003c\/p\u003e \u003cp\u003e6.6 An Alternative Analysis Method.\u003c\/p\u003e \u003cp\u003e6.7 Analysis Strategies for Multiple Responses I: Out-of-Spec Probabilities.\u003c\/p\u003e \u003cp\u003e6.8 Blocking in 3\u003csup\u003ek\u003c\/sup\u003e and 3\u003csup\u003ek-p\u003c\/sup\u003e Designs.\u003c\/p\u003e \u003cp\u003e6.9 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Other Design and Analysis Techniques for Experiments at More Than Two Levels.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design.\u003c\/p\u003e \u003cp\u003e7.2 Method of Replacement and Construction of 2\u003csup\u003em\u003c\/sup\u003e4\u003csup\u003en\u003c\/sup\u003e Designs.\u003c\/p\u003e \u003cp\u003e7.3 Minimum Aberration 2\u003csup\u003em\u003c\/sup\u003e4\u003csup\u003en\u003c\/sup\u003e Designs with \u003ci\u003en\u003c\/i\u003e = 1, 2.\u003c\/p\u003e \u003cp\u003e7.4 An Analysis Strategy for 2\u003csup\u003em\u003c\/sup\u003e4\u003csup\u003en\u003c\/sup\u003e Experiments.\u003c\/p\u003e \u003cp\u003e7.5 Analysis of the Router Bit Experiment.\u003c\/p\u003e \u003cp\u003e7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design.\u003c\/p\u003e \u003cp\u003e7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels.\u003c\/p\u003e \u003cp\u003e7.8 \u003ci\u003er\u003c\/i\u003e\u003csup\u003ek-p\u003c\/sup\u003e Fractional Factorial Designs for any Prime Number \u003ci\u003er\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e7.9 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling.\u003c\/p\u003e \u003cp\u003e7.10 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonregular Designs: Construction and Properties.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing.\u003c\/p\u003e \u003cp\u003e8.2 Some Advantages of Nonregular Designs Over the 2\u003csup\u003ek-p\u003c\/sup\u003e and 3\u003csup\u003ek-p\u003c\/sup\u003e Series of Designs.\u003c\/p\u003e \u003cp\u003e8.3 A Lemma on Orthogonal Arrays.\u003c\/p\u003e \u003cp\u003e8.4 Plackett-Burman Designs and Hall's Designs.\u003c\/p\u003e \u003cp\u003e8.5 A Collection of Useful Mixed-Level Orthogonal Arrays.\u003c\/p\u003e \u003cp\u003e8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices.\u003c\/p\u003e \u003cp\u003e8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement.\u003c\/p\u003e \u003cp\u003e8.8 Orthogonal Main-Effect Plans Through Collapsing Factors.\u003c\/p\u003e \u003cp\u003e8.9 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Experiments with Complex Aliasing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Partial Aliasing of Effects and the Alias Matrix.\u003c\/p\u003e \u003cp\u003e9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis.\u003c\/p\u003e \u003cp\u003e9.3 Simplification of Complex Aliasing via Effect Sparsity.\u003c\/p\u003e \u003cp\u003e9.4 An Analysis Strategy for Designs with Complex Aliasing.\u003c\/p\u003e \u003cp\u003e9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing.\u003c\/p\u003e \u003cp\u003e9.6 Supersaturated Designs: Design Construction and Analysis.\u003c\/p\u003e \u003cp\u003e9.7 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Response Surface Methodology.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 A Ranitidine Separation Experiment.\u003c\/p\u003e \u003cp\u003e10.2 Sequential Nature of Response Surface Methodology.\u003c\/p\u003e \u003cp\u003e10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search.\u003c\/p\u003e \u003cp\u003e10.4 Analysis of Second-Order Response Surfaces.\u003c\/p\u003e \u003cp\u003e10.5 Analysis of the Ranitidine Experiment.\u003c\/p\u003e \u003cp\u003e10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions.\u003c\/p\u003e \u003cp\u003e10.7 Central Composite Designs.\u003c\/p\u003e \u003cp\u003e10.8 Box-Behnken Designs and Uniform Shell Designs.\u003c\/p\u003e \u003cp\u003e10.9 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Introduction to Robust Parameter Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments.\u003c\/p\u003e \u003cp\u003e11.2 Strategies for Reducing Variation.\u003c\/p\u003e \u003cp\u003e11.3 Noise (Hard-to-Control) Factors.\u003c\/p\u003e \u003cp\u003e11.4 Variation Reduction Through Robust Parameter Design.\u003c\/p\u003e \u003cp\u003e11.5 Experimentation and Modeling Strategies I: Cross Array.\u003c\/p\u003e \u003cp\u003e11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling.\u003c\/p\u003e \u003cp\u003e11.7 Cross Arrays: Estimation Capacity and Optimal Selection.\u003c\/p\u003e \u003cp\u003e11.8 Choosing Between Cross Arrays and Single Arrays.\u003c\/p\u003e \u003cp\u003e11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization.\u003c\/p\u003e \u003cp\u003e11.10 Further Topics.\u003c\/p\u003e \u003cp\u003e11.11 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Robust Parameter Design for Signal-Response Systems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 An Injection Molding Experiment.\u003c\/p\u003e \u003cp\u003e12.2 Signal-Response Systems and their Classification.\u003c\/p\u003e \u003cp\u003e12.3 Performance Measures for Parameter Design Optimization.\u003c\/p\u003e \u003cp\u003e12.4 Modeling and Analysis Strategies.\u003c\/p\u003e \u003cp\u003e12.5 Analysis of the Injection Molding Experiment.\u003c\/p\u003e \u003cp\u003e12.6 Choice of Experimental Plans.\u003c\/p\u003e \u003cp\u003e12.7 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Experiments for Improving Reliability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Experiments with Failure Time Data.\u003c\/p\u003e \u003cp\u003e13.2 Regression Model for Failure Time Data.\u003c\/p\u003e \u003cp\u003e13.3 A Likelihood Approach for Handling Failure Time Data with Censoring.\u003c\/p\u003e \u003cp\u003e13.4 Design-Dependent Model Selection Strategies.\u003c\/p\u003e \u003cp\u003e13.5 A Bayesian Approach to Estimation and Model Selection for Failure Time Data.\u003c\/p\u003e \u003cp\u003e13.6 Analysis of Reliability Experiments with Failure Time Data.\u003c\/p\u003e \u003cp\u003e13.7 Other Types of Reliability Data.\u003c\/p\u003e \u003cp\u003e13.8 Practical Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Analysis of Experiments with Nonnormal Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A Wave Soldering Experiment with Count Data.\u003c\/p\u003e \u003cp\u003e14.2 Generalized Linear Models.\u003c\/p\u003e \u003cp\u003e14.3 Likelihood-Based Analysis of Generalized Linear Models.\u003c\/p\u003e \u003cp\u003e14.4 Likelihood-Based Analysis of the Wave Soldering Experiment.\u003c\/p\u003e \u003cp\u003e14.5 Bayesian Analysis of Generalized Linear Models.\u003c\/p\u003e \u003cp\u003e14.6 Bayesian Analysis of the Wave Soldering Experiment.\u003c\/p\u003e \u003cp\u003e14.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data.\u003c\/p\u003e \u003cp\u003e14.8 Modeling and Analysis for Ordinal Data.\u003c\/p\u003e \u003cp\u003e14.9 Analysis of Foam Molding Experiment.\u003c\/p\u003e \u003cp\u003e14.10 Scoring: A Simple Method for Analyzing Ordinal Data.\u003c\/p\u003e \u003cp\u003e14.11 Practical Summary.\u003c\/p\u003e \u003cp\u003eAppendix A Upper Tail Probabilities of the Standard Normal Distribution.\u003c\/p\u003e \u003cp\u003eAppendix B Upper Percentiles of the t Distribution.\u003c\/p\u003e \u003cp\u003eAppendix C Upper Percentiles of the χ\u003csup\u003e2\u003c\/sup\u003e Distribution.\u003c\/p\u003e \u003cp\u003eAppendix D Upper Percentiles of the F Distribution.\u003c\/p\u003e \u003cp\u003eAppendix E Upper Percentiles of the Studentized Range Distribution.\u003c\/p\u003e \u003cp\u003eAppendix F Upper Percentiles of the Studentized Maximum Modulus Distribution.\u003c\/p\u003e \u003cp\u003eAppendix G Coefficients of Orthogonal Contrast Vectors.\u003c\/p\u003e \u003cp\u003eAppendix H Critical Values for Lenth's Method.\u003c\/p\u003e \u003cp\u003eAuthor Index.\u003c\/p\u003e \u003cp\u003eSubject Index. \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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