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Despite recent expansion of genome-wide association studies, methodologies for statistical analysis of the resulting data are still lagging behind.
Functional data analysis encompasses many methodologies for statistical analysis of data coming from measurements concerning continuous phenomena, e.g., curves or surfaces [ 47].
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13065_2012_373_MOESM1_ESM.pdf Additional file 1: Methodology for statistical analysis. PDF 301 KB) 13065_2012_373_MOESM2_ESM.pdf Additional file 2: Methodology for cost benefit analysis. PDF 336 KB).
The current work develops a rigorous methodology for statistical inference on available data that accounts for the limited nature of experimental observations.
It is rather a general methodology for statistical elimination of redundant metabolic structures that, in a broader sense, has the potential to bring together all layers of 'omic' information under a common computational framework.
Methodologies for assessing statistical and clinical significance of intervention effects in systematic reviews were considered.
The methodology used for statistical analysis is described in Smyth [ 76].
Despite this, the field has not yet defined accepted methodologies and standards for statistical testing of variants on a whole genome scale.
The methodology for both statistical and instantaneous buffer information is provided.
In this paper we propose a methodology for the statistical generalization of the available sensorimotor knowledge in real-time.
The choice of a methodology for testing statistical significance also has a strong effect on what is estimated as the LOEC.
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