Design and Analysis of Experiments with RCRC Press, 2014 M12 17 - 620 páginas Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, |
Contenido
1 Introduction | 1 |
2 Completely Randomized Designs with One Factor | 17 |
3 Factorial Designs | 55 |
4 Randomized Block Designs | 113 |
5 Designs to Study Variances | 141 |
6 Fractional Factorial Designs | 193 |
7 Incomplete and Confounded Block Designs | 261 |
8 SplitPlot Designs | 307 |
10 Response Surface Designs | 383 |
11 Mixture Experiments | 447 |
12 Robust Parameter Design Experiments | 503 |
13 Experimental Strategies for Increasing Knowledge | 557 |
| 571 | |
| 573 | |
Bibliography | 577 |
| 593 | |
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Términos y frases comunes
AlgDesign analysis ANOVA ANOVA table batch block defining contrasts block design blocking factor cell means central composite design column completely randomized completely randomized design control factors created D-optimal daewr package data frame defining relation degrees of freedom determine Equation estimates example experimental design experimental error experimental region experimental units F-test factor array factorial experiment fractional factorial design FrF2 half-normal plot least squares library(daewr linear model lmer lurking variables main effects matrix mean square method of moments mixture components mixture experiments noise factors normal plot number of levels number of replicates optimal P-values Plackett-Burman design predicted process variables quadratic model REML represent residuals response surface design Scheffé shown in Figure shown in Table significant split-plot design split-plot experiment Statistics sub-plot factors subset sums of squares temperature treatment combinations treatment effects treatment factor treatment level two-factor interactions value Pr(>F variance components whole-plot factors σ²