The Design and Analysis of Factorial Experiments

@article{Fisher1938TheDA,
  title={The Design and Analysis of Factorial Experiments},
  author={Rory A. Fisher},
  journal={Nature},
  year={1938},
  volume={142},
  pages={90-92}
}
THE publications of the various Imperial Bureaux are necessarily of very unequal scientific value, and naturally also, appeal to very different bodies of scientific workers. Their format and presentation are not such as to excite an expectation of material of wide interest. Thus, the reader who encounters first in large letters "Imperial Bureau of Soil Science", and then the somewhat repellent caption "Technical Communication No. 35", is not well prepared for the exceptional interest of the… 
Affirmation of the Classical Terminology for Experimental Design via a Critique of Casella’s Statistical Design
TLDR
Key issues are the tripartite structure of the design of an experiment, the need for experimental units to be physically independent of each other, the definition of pseudoreplication, and confusion about the meaning of split-unit designs.
A unifying computational method for the analysis of complete factorial experiments
A computational method which may be used for the calculation of sums of squares in the analysis of variance of complete factorial experiments and in the computation of main effect or interaction
Equivalences in design of experiments
The statistical theory of experimental designs was initiated by Fisher in the 1920s in the context of agricultural experiments performed at the Rothamsted Experimental Station. Applications of
2 Experimental Design
Sir Ronald Fisher, the statistician, eugenicist, evolutionary biologist, geneticist, and father of modern experimental design, observed that experiments are ‘only experience carefully planned in
Automatic generation of generalised regular factorial designs
Analysis of variance in soil research: let the analysis fit the design
Sound design for experiments on soil is based on two fundamental principles: replication and randomization. Replication enables investigators to detect and measure contrasts between treatments
Random effects models for complex designs
TLDR
The development of random effects models is outlined and the potential importance of block-treatment interactions is highlighted, and the use of a variety of techniques is shown to lead to a better understanding of the study.
Innovations in Randomization Inference for the Design and Analysis of Experiments and Observational Studies
TLDR
This dissertation proposes how to implement rerandomization in factorial experiments, extends the theoretical properties of re randomization from single-factor experiments to 2 factorial designs, and demonstrates how a designed experiment can improve precision of estimated factorial effects.
Remember a pioneer: Frank Yates (1902‐1994)
OST teachers or practitioners of statistical M science will be familiar with the Yates Continuity Correction, used in testingthe significance of association in a 2x2 table of frequencies, and with
IsoCheck: An R Package to check Isomorphism for Two-level Factorial Designs with Randomization Restrictions
Factorial designs are often used in various industrial and sociological experiments to identify significant factors and factor combinations that may affect the process response. In the statistics
...
...