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Biometrics, 1967
A split plot design for the factorial treatment combinations of factors A and B with t and s levels respectively, consists of wholeplots made up of s subplots or experimental units with each level of A applied to r wholeplots and the levels of B applied to the s subplots within each wholeplot.
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A split plot design for the factorial treatment combinations of factors A and B with t and s levels respectively, consists of wholeplots made up of s subplots or experimental units with each level of A applied to r wholeplots and the levels of B applied to the s subplots within each wholeplot.
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On the Analysis of Split-Plot Experiments
Biometrics, 1961A crucial question in the analysis of split-plot experiments is whether or not the interaction between subplot treatments and replications should be pooled with the three-factor interaction of main-plot treatments, subplot treatments, and replications, the result being called subplot error.
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1991
Here we introduce the simplest “hierarchical” design, the split plot design. This design has two error terms, corresponding to a subdivision of the error space into two orthogonal subspaces. Studies employing this design have (at least) two treatment factors; the effects of one factor, however, are estimated more accurately than the effects of the ...
David J. Saville, Graham R. Wood
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Here we introduce the simplest “hierarchical” design, the split plot design. This design has two error terms, corresponding to a subdivision of the error space into two orthogonal subspaces. Studies employing this design have (at least) two treatment factors; the effects of one factor, however, are estimated more accurately than the effects of the ...
David J. Saville, Graham R. Wood
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2017
When the levels of some treatment factors are more difficult to change during the experiment than those of others, split-plot designs are necessary. In a split-plot design, the experimental units are called split plots, and are nested within whole plots, which themselves may or may not be nested within blocks. The split plots within each whole plot are
Angela Dean +2 more
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When the levels of some treatment factors are more difficult to change during the experiment than those of others, split-plot designs are necessary. In a split-plot design, the experimental units are called split plots, and are nested within whole plots, which themselves may or may not be nested within blocks. The split plots within each whole plot are
Angela Dean +2 more
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1987
In an experiment with at least two factors, it is sometimes convenient to apply some of the factors to large experimental units (called whole plots) and then to split the large units into smaller parts on which the remaining factors are applied. The subdivisions of the whole plots are called subplots or split plots.
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In an experiment with at least two factors, it is sometimes convenient to apply some of the factors to large experimental units (called whole plots) and then to split the large units into smaller parts on which the remaining factors are applied. The subdivisions of the whole plots are called subplots or split plots.
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A Split-Plot Analysis for Microarray Experiments
2004http:\\digital.casalini.it ...
BERNI, ROSSELLA +1 more
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Blocking in incomplete split plot designs
Biometrika, 1970SUMMARY An incomplete split plot design with whole plots arranged in a completely randomized design was proposed by Robinson (1967). In this note, designs in which the whole plots are arranged in blocks, are considered. A method of construction and estimates of treatment effects are given. Robinson (1967) discussed certain incomplete split plot designs
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2014
For the split-plot design, we are concerned with two or more factors, but we wish for more precise information on some of them than on others. If we are interested in more accurate information, for instance, on factor B than on A, then the usual scheme is to assign the various levels of factor A at random to whole plots (main plots) in each replicate ...
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For the split-plot design, we are concerned with two or more factors, but we wish for more precise information on some of them than on others. If we are interested in more accurate information, for instance, on factor B than on A, then the usual scheme is to assign the various levels of factor A at random to whole plots (main plots) in each replicate ...
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