This paper studies the impact when the experimenter does not randomize the design for split-plot experiments when there is a linear trend. The paper uses both simulation and basic theory to explain the bias if we do not randomize the design and the inflation in variance if we do. The paper shows proper residual plots can detect the trend if the design is randomized, which allows the analyst to treat time as a covariate, removing the trend's impact. The simulation study provides an excellent way to explain to practitioners the risks from not randomizing the design.
A Tutorial on Randomizing versus Not Randomizing Split-Plot Experiments / Berni Rossella, Bertocci Francesco, Nikiforova Nedka Dechkova, Vining Geoffrey G.. - In: QUALITY ENGINEERING. - ISSN 0898-2112. - STAMPA. - 32:(2020), pp. 25-45. [10.1080/08982112.2019.1617422]
A Tutorial on Randomizing versus Not Randomizing Split-Plot Experiments
Berni Rossella
Writing – Original Draft Preparation
;Nikiforova Nedka DechkovaWriting – Original Draft Preparation
;
2020
Abstract
This paper studies the impact when the experimenter does not randomize the design for split-plot experiments when there is a linear trend. The paper uses both simulation and basic theory to explain the bias if we do not randomize the design and the inflation in variance if we do. The paper shows proper residual plots can detect the trend if the design is randomized, which allows the analyst to treat time as a covariate, removing the trend's impact. The simulation study provides an excellent way to explain to practitioners the risks from not randomizing the design.File | Dimensione | Formato | |
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