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Forced oscillations can be detected using statistical properties of a periodogram.
These results confirm the high prevalence of evolutionary modifications within our set of predicted motifs and suggest that regulatory element modifications can be detected using statistical methods based upon different underlying assumptions.
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The variability introduced by single individuals was detected using statistical random effects models.
Differential gene expression can thus be detected using robust statistical evidence, even for genes with low expression differences, and the method described above provides us with a more complete picture of the transcriptomic response.
The study demonstrated that subtle differences can be detected using the current methods and statistical treatment of the data.
Genomic regions associated with calving difficulty (direct and maternal) and perinatal mortality were detected using two statistical approaches: (1) single-SNP (single nucleotide polymorphism) regression and (2) a Bayesian approach.
A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches.
All polymorphisms were detected using PCR-RFLP and statistical analysis done with χ2 test and ANOVA.
Significant differential gene expression levels were detected using mixed effects modeling and Statistical Analysis of Microarray (SAM).
Statistical significance was detected using the independent-samples t test and analysis of variance at α = 0.05.
No statistical differences were detected using the Fast UniFrac P Test (Table 4 – below values), which revealed that the bacterial communities of the samples were similar.
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