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After descriptive analyses of data using standard summary measures and graphical representation, two least-squares models were established, with total bacterial count and individual bacterial species count as the outcomes.
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Introduced is a general framework for performing group-level analyses of fMRI data using any basis set of two functions (i.e., the canonical hemodynamic response function and its first derivative) to model the hemodynamic response to neural activity.
The data-driven approaches involve the gathering of large-scale omics data sets and subsequent analyses of these data using statistical modeling techniques.
In the present investigation, retrospective analyses of microbiology data using a statistical technique were promising in terms of identifying plausible C. difficile clusters.
Pending student interest, analyses of these data using R statistical packages, ArcView and/or ArcGIS.
Analyses of the data using t-tests revealed significant reductions in indecision, anxiety, uncertainty, and insecurity for the intervention group but not for the control group.
Statistical analyses of these data using a students' t-test was conducted.
For each malaria phenotype, we first performed statistical analyses of the data using multivariate regression analysis to take into account non-genetic factors such as age, year of study and transmission intensity (see Materials and Methods).
We also conducted alternative analyses of the data using the Wilcoxon signed rank test and the paired t-test.
In prior analyses of these data using a slightly different approach, γ-chlordane and DDE were both associated with significantly increased risks of NHL Coltt et al. 2005, 2006).
To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published.
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