Split-plot designs are frequently needed in practice because of practical limitations and issues related to cost. This imposes extra challenges on the experimenter, both when designing the experiment and when analysing the data, in particular for non-replicated cases. This paper is an overview and discussion of some of the most important methods for analysing split-plot data. The focus is on estimation, testing and model validation. Two examples from an industrial context are given to illustrate the most important techniques. Copyright (c) 2006 John Wiley & Sons, Ltd.
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Qufu Normal Univ, Sch Stat & Data Sci, Qufu 273165, Shandong, Peoples R ChinaQufu Normal Univ, Sch Stat & Data Sci, Qufu 273165, Shandong, Peoples R China
Hu, Minyang
Zhao, Shengli
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Qufu Normal Univ, Sch Stat & Data Sci, Qufu 273165, Shandong, Peoples R ChinaQufu Normal Univ, Sch Stat & Data Sci, Qufu 273165, Shandong, Peoples R China
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Department of Mathematics, National Technical University of Athens, Zografou, AthensDepartment of Mathematics, National Technical University of Athens, Zografou, Athens
Drosou K.
Koukouvinos C.
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Department of Mathematics, National Technical University of Athens, Zografou, AthensDepartment of Mathematics, National Technical University of Athens, Zografou, Athens
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Temple Univ, Fox Business Sch Management, Dept Stat, Philadelphia, PA 19122 USATemple Univ, Fox Business Sch Management, Dept Stat, Philadelphia, PA 19122 USA
Raghavarao, D
Yang, X
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Temple Univ, Fox Business Sch Management, Dept Stat, Philadelphia, PA 19122 USATemple Univ, Fox Business Sch Management, Dept Stat, Philadelphia, PA 19122 USA