The concise yet authoritative presentation of key techniques for basic mixtures experiments Inspired by the author's bestselling advanced book on the topic, A Primer on Experiments with Mixtures provides an introductory presentation of the key principles behind experimenting with mixtures. Outlining useful techniques through an applied approach with examples from real research situations, the book supplies a comprehensive discussion of how to design and set up basic mixture experiments, then analyze the data and draw inferences from results. Drawing from his extensive experience teaching the topic at various levels, the author presents the mixture experiments in an easy-to-follow manner that is void of unnecessary formulas and theory. Succinct presentations explore key methods and techniques for carrying out basic mixture experiments, including: * Designs and models for exploring the entire simplex factor space, with coverage of simplex-lattice and simplex-centroid designs, canonical polynomials, the plotting of individual residuals, and axial designs * Multiple constraints on the component proportions in the form of lower and/or upper bounds, introducing L-Pseudocomponents, multicomponent constraints, and multiple lattice designs for major and minor component classifications * Techniques for analyzing mixture data such as model reduction and screening components, as well as additional topics such as measuring the leverage of certain design points * Models containing ratios of the components, Cox's mixture polynomials, and the fitting of a slack variable model * A review of least squares and the analysis of variance for fitting data Each chapter concludes with a summary and appendices with details on the technical aspects of the material. Throughout the book, exercise sets with selected answers allow readers to test their comprehension of the material, and References and Recommended Reading sections outline further resources for study of the presented topics. A Primer on Experiments with Mixtures is an excellent book for one-semester courses on mixture designs and can also serve as a supplement for design of experiments courses at the upper-undergraduate and graduate levels. It is also a suitable reference for practitioners and researchers who have an interest in experiments with mixtures and would like to learn more about the related mixture designs and models.

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InhaltsangabePreface ix 1. Introduction 1 1.1 The Original Mixture Problem, 2 1.2 A Pesticide Example Involving Two Chemicals, 2 1.3 General Remarks About Response Surface Methods, 9 1.4 An Historical Perspective, 13 References and Recommended Reading, 17 Questions, 17 Appendix 1A: Testing for Nonlinear Blending of the Two Chemicals Vendex and Kelthane While Measuring the Average Percent Mortality (APM) of Mites, 20 2. The Original Mixture Problem: Designs and Models for Exploring the Entire Simplex Factor Space 23 2.1 The Simplex-Lattice Designs, 23 2.2 The Canonical Polynomials, 26 2.3 The Polynomial Coefficients As Functions of the Responses at the Points of the Lattices, 31 2.4 Estimating The Parameters in the {q,m} Polynomials, 34 2.5 Properties of the Estimate of the Response, y(x), 37 2.6 A ThreeComponent Yarn Example Using A {3, 2} SimplexLattice Design, 38 2.7 The Analysis of Variance Table, 42 2.8 Analysis of Variance Calculations of the Yarn Elongation Data, 45 2.9 The Plotting of Individual Residuals, 48 2.10 Testing the Degree of the Fitted Model: A Quadratic Model or Planar Model?, 49 2.11 Testing Model Lack of Fit Using Extra Points and Replicated Observations, 55 2.12 The Simplex-Centroid Design and Associated Polynomial Model, 58 2.13 An Application of a Four-Component Simplex-Centroid Design: Blending Chemical Pesticides for Control of Mites, 60 2.14 Axial Designs, 62 2.15 Comments on a Comparison Made Between An Augmented Simplex-Centroid Design and a Full Cubic Lattice for Three Components Where Each Design Contains Ten Points, 66 2.16 Reparameterizing Scheffe's Mixture Models to Contain A Constant (²0) Term: A Numerical Example, 69 2.17 Questions to Consider at the Planning Stages of a Mixture Experiment, 77 2.18 Summary, 78 References and Recommended Reading, 78 Questions, 80 Appendix 2A: Least-Squares Estimation Formula for the Polynomial Coefficients and Their Variances: Matrix Notation, 85 Appendix 2B: Cubic and Quartic Polynomials and Formulas for the Estimates of the Coefficients, 90 Appendix 2C: The Partitioning of the Sources in the Analysis of Variance Table When Fitting the Scheff´e Mixture Models, 91 3. Multiple Constraints on the Component Proportions 95 3.1 LowerBound Restrictions on Some or All of the Component Proportions, 95 3.2 Introducing L-Pseudocomponents, 97 3.3 A Numerical Example of Fitting An L-Pseudocomponent Model, 99 3.4 UpperBound Restrictions on Some or All Component Proportions, 101 3.5 An Example of the Placing of an Upper Bound on a Single Component: The Formulation of a Tropical Beverage, 103 3.6 Introducing U-Pseudocomponents, 107 3.7 The Placing of Both Upper and Lower Bounds on the Component Proportions, 112 3.8 Formulas For Enumerating the Number of Extreme Vertices, Edges, and Two-Dimensional Faces of the Constrained Region, 119 3.9 McLean and Anderson's Algorithm For Calculating the Coordinates of the Extreme Vertices of a Constrained Region, 123 3.10 Multicomponent Constraints, 128 3.11 Some Examples of Designs for Constrained Mixture Regions: CONVRT and CONAEV Programs, 131 3.12 Multiple Lattices for Major and Minor Component Classifications, 138 Summary, 154 References and Recommended Reading, 155 Questions, 157 4. The Analysis of Mixture Data 159 4.1 Techniques Used in the Analysis of Mixture Data, 160 4.2 Test Statistics for Testing the Usefulness of the Terms in the Scheff´e Polynomials, 163 4.3 Model Reduction, 170 4.4 An Example of Reducing the System from Three to Two Components, 173 4.5 Screening Components, 175 4.6 Other Techniques Used to Measure Component Effects, 179 4.7 Leverage and the Hat Matrix, 190 4.8 A ThreeComponent Propellant Example, 192 4.9 Summary, 195 References and Recommended Reading, 196 Questions, 197 5. Other Mixture Model Forms 201 5.1 The Inclusion of Inverse

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