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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Univ New England, Dept Math Sci, 11 Hills Beach Rd, Biddeford, ME 04005 USAUniv New England, Dept Math Sci, 11 Hills Beach Rd, Biddeford, ME 04005 USA
Koh, Woon Yuen
Eskridge, Kent M.
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Univ Nebraska, Dept Statistics, Lincoln, NE 68583 USAUniv New England, Dept Math Sci, 11 Hills Beach Rd, Biddeford, ME 04005 USA
Eskridge, Kent M.
Hanna, Milford A.
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Univ Nebraska, Dept Biol Syst Engn, Lincoln, NE 68583 USAUniv New England, Dept Math Sci, 11 Hills Beach Rd, Biddeford, ME 04005 USA
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Visva Bharati Univ, Dept Stat, Santini Ketan, West Bengal, IndiaVisva Bharati Univ, Dept Stat, Santini Ketan, West Bengal, India
Chatterjee, K.
Koukouvinos, C.
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Natl Tech Univ Athens, Dept Math, Athens 15773, GreeceVisva Bharati Univ, Dept Stat, Santini Ketan, West Bengal, India
Koukouvinos, C.
Mylona, K.
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Kings Coll London, Dept Math, London WC2R 2LS, England
Univ Carlos III Madrid, Dept Stat, Calle Madrid 126, Getafe, SpainVisva Bharati Univ, Dept Stat, Santini Ketan, West Bengal, India