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This article highlights various statistical procedures for analyzing data from activation studies and functional connectivity studies, focusing on functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data.
Various statistical procedures have been suggested for analyzing data from multiple-group repeated measures (i.e., split-plot) designs when parametric model assumptions are violated (e.g., Akritas and Arnold (J. Amer. Statist. Assoc. 89 (1994) 336); Brunner and Langer (Biometrical J. 42 (2000) 663)), including the use of Friedman ranks.
Various statistical procedures will be applied, depending on the scale level in each case.
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Several statistical procedures were taken to validate the scale.
It is usually complemented by statistical procedures that support the various stages of the data analysis process [ 1].
The similarity of both the shape of the distributions and the various statistical indices supports the outcomes of the procedure employed.
At every stage of the process, statistical procedures were used to model various parameters and validate the model.
We identified various challenges in data standardization and selection of appropriate statistical procedures.
For all statistical procedures, P < 0.05 was considered statistically significant.
Later, automatic procedures can be applied to determine desired measurements and various statistical estimates.
In contrast to those four GeneChip reports where various statistical methods and fold-change cutoffs were used, we used the same procedure for the analysis of all of the transcriptome datasets.
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