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The design and analysis of experiments.
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A Symbolic Logic for Representing Linear Models.
TL;DR: In this paper, a set of symbols along with logical operators are used to represent the possible manipulations of the linear model and the use of these symbols and operators is to simplify the representation of analysis of variance models, correlation models and factor analysis models.
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On randomization, modeling and experimental design: some remarks on a paper by De Vos
TL;DR: In this paper, it is shown that De Vos' example is not new; the gains in efficiency are exaggerated due to comparison with an inefficient randomization method; and the paper is over-optimistic with respect to robustness of model-based methods and throws unjustified doubts on the validity of randomization methods.
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Rerandomization and Regression Adjustment
Xinran Li,Peng Ding +1 more
TL;DR: In this paper, the authors show that combining rerandomization and regression adjustment yields better coverage properties and thus improves statistical inference, and then measure the additional gains of the designer and the analyzer.
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Characterizing Variability in Smestad and Gratzel's Nanocrystalline Solar Cells: A Collaborative Learning Experience in Experimental Design.
TL;DR: In this article, statistics majors worked with a teaching assistant from the chemistry department to conduct a series of experiments characterizing the variation in measured voltage output of Smestad and Gratzel's nanocrystaline titanium dioxide (TiO2) solar cells.
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Value Differences Among Public High School Teachers Using a Regression Model Analysis of Variance Technique
TL;DR: For example, Thorndike and Hagen as discussed by the authors found significant differences across the occupations as well as considerable differences within certain groups within certain occupational groups, such as socialstudies teachers and social-welfare work-enders.