Abstract
In an one-way analysis of variance with standard assumptions suppose that only one observation exists per treatment. In addition, assume that one of the treatments is a control group. Because of insufficient observations, the variance of the populations cannot be estimated and hence the usual methods for comparing treatments with the control group fail. In this paper, we present a method to compare treatments with a control when one observation exits per treatment. An algorithm is given to estimate the critical values of the test. The power of the test is investigated by a Monte Carlo simulation; numerical studies show that when there is a treatment whose mean is close to the control group, the power of the test is satisfactory.
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References
Chen Y, Kunert J (2004) A new quantitatively method for analyzing unreplicated factorial designs. Biom J 46: 125–140
Kharrati-Kopaei M, Sadooghi-Alvandi SM (2007) A new method for testing interaction in unreplicated two-way analysis of variance. Commun Stat Theory Methods 36: 2787–2804
Montgomery DC (2001) Design and analysis of experiments, fifth edition. Wiley, INC, p 174
Ostle B (1963) Statistics in research, basic concepts and techniques for research works, second edition. The Iowa State University Press, Ames, p 396
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Kharrati-Kopaei, M. Comparing means of several treatments with a control when one observation exists per treatment. Metrika 70, 165–176 (2009). https://doi.org/10.1007/s00184-008-0185-4
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DOI: https://doi.org/10.1007/s00184-008-0185-4