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Sentences for "bivariate "
• The highest fold change (HFC) of gene k is defined as the following: When visualizing all genes on a bivariate plot according to absolute expression and fold change, one obtains a data distribution similar to that of Figure 1a.
This analysis, called a bivariate analysis [ 32 ] , represents an individual specimen as a vector in the x-y plane.
When these same luc records are plotted using the bivariate method as shown in Figures 9, 10, both the temporal distribution and the intraspecimen variability determine the placement of individual points within the circle (on the right in Figure 10e).
The second approach uses a bivariate analysis that reveals intraspecimen variability as well as mean peak time to test differences in phase.
Therefore, we have also introduced another approach using bivariate statistics to represent the molecular cycles more completely [ 32 ] .
We calculated ORS and 95% CIs for the bivariate analyses using EpiInfo [ 23 ] , all other analyses and data management were done using SAS [ 24 ] .
Other variables associated in the bivariate analyses were added one at a time to the base model.
The relation of the two dichotomous dependence outcomes (>12 hours supervision and dependence level>=3) to potential predictors, including cognitive function, was investigated initially in bivariate analyses.
For one bivariate analysis where cells contained zero, unmatched, median-unbiased odds ratios and exact 95% confidence intervals were calculated from the mid-p function as described by Rothman [ 22 ] .
Other routes of fecal contamination were not associated with cryptosporidiosis in the bivariate and multivariable analyses.
Sex with a partner with diarrhea was moderately associated in the bivariate analysis (OR, 2.
In bivariate analyses, one's opinion of the risk of a smallpox attack was strongly associated with willingness or desire to be immunized (p < .
We evaluated the bivariate relationships between gender and outcome with selected baseline covariates, and constructed a time-series repeated measures regression model [ 20 21 ] to describe the influence of gender on the outcome using data at baseline and two, six, nine and 12 months.
Based on the approach used in the development of previous bivariate population models [ 12 13 14 ] , we developed and applied a model testing for the direct influence of large-scale climate on the dynamics at individual trophic levels and interactions among levels (Figure 1).
The main question therefore was: How does the univariate binary RSA pattern analysis (Method 2) correspond to the bivariate analysis of phase relations between the cardiac and the respiratory cycle (Method 1)?
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