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Preface | |
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Acknowledgments | |
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A simple comparative experiment | |
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Key concepts | |
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The setup of a comparative experiment | |
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Summary | |
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An optimal screening experiment | |
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Key concepts | |
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Case: an extraction experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Main-effects models | |
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Models with two-factor interaction effects | |
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Factor scaling | |
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Ordinary least squares estimation | |
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Significance tests and statistical power calculations | |
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Variance inflation | |
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Aliasing | |
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Optimal design | |
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Generating optimal experimental designs | |
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The extraction experiment revisited | |
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Principles of successful screening: sparsity, hierarchy, and heredity | |
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Background reading | |
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Screening | |
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Algorithms for finding optimal designs | |
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Summary | |
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Adding runs to a screening experiment | |
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Key concepts | |
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Case: an augmented extraction experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Optimal selection of a follow-up design | |
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Design construction algorithm | |
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Foldover designs | |
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Background reading | |
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Summary | |
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A response surface design with a categorical factor | |
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Key concepts | |
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Case: a robust and optimal process experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Quadratic effects | |
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Dummy variables for multilevel categorical factors | |
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Computing D-efficiencies | |
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Constructing Fraction of Design Space plots | |
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Calculating the average relative variance of prediction | |
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Computing I-efficiencies | |
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Ensuring the validity of inference based on ordinary least squares | |
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Design regions | |
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Background reading | |
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Summary | |
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A response surface design in an irregularly shaped design region | |
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Key concepts | |
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Case: the yield maximization experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Cubic factor effects | |
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Lack-of-fit test | |
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Incorporating factor constraints in the design construction algorithm | |
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Background reading | |
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Summary | |
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A "mixture" experiment with process variables | |
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Key concepts | |
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Case: the rolling mill experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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The mixture constraint | |
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The effect of the mixture constraint on the model | |
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Commonly used models for data from mixture experiments | |
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Optimal designs for mixture experiments | |
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Design construction algorithms for mixture experiments | |
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Background reading | |
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Summary | |
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A response surface design in blocks | |
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Key concepts | |
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Case: the pastry dough experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Model | |
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Generalized least squares estimation | |
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Estimation of variance components | |
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Significance tests | |
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Optimal design of blocked experiments | |
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Orthogonal blocking | |
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Optimal versus orthogonal blocking | |
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Background reading | |
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Summary | |
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A screening experiment in blocks | |
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Key concepts | |
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Case: the stability improvement experiment | |
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Problem and design | |
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Afterthoughts about the design problem | |
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Data analysis | |
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Peek into the black box | |
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Models involving block effects | |
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Fixed block effects | |
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Background reading | |
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Summary | |
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Experimental design in the presence of covariates | |
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Key concepts | |
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Case: the polypropylene experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Covariates or concomitant variables | |
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Models and design criteria in the presence of covariates | |
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Designs robust to time trends | |
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Design construction algorithms | |
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To randomize or not to randomize | |
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Final thoughts | |
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Background reading | |
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Summary | |
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A split-plot design | |
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Key concepts | |
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Case: the wind tunnel experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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Split-plot terminology | |
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Model | |
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Inference from a split-plot design | |
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Disguises of a split-plot design | |
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Required number of whole plots and runs | |
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Optimal design of split-plot experiments | |
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A design construction algorithm for optimal split-plot designs | |
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Difficulties when analyzing data from split-plot experiments | |
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Background reading | |
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Summary | |
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A two-way split-plot design | |
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Key concepts | |
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Case: the battery cell experiment | |
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Problem and design | |
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Data analysis | |
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Peek into the black box | |
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The two-way split-plot model | |
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Generalized least squares estimation | |
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Optimal design of two-way split-plot experiments | |
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A design construction algorithm for D-optimal two-way split-plot designs | |
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Extensions and related designs | |
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Background reading | |
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Summary | |
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Bibliography | |
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Index | |