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The agreement between the peaks identified in our data and those reported by Demicheli and coworkers [ 5] becomes even more apparent in the corresponding kernel plot of Figure 3a.
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All statistical analysis was completed using the Stata statistical software package (release 7.0; Stata Corporation, College Station, TX, USA), in particular the function sthaz for the kernel plots.
Normality of continuous variables were evaluated with the use of kernel density plots and plots comparing quantiles of each of these variables versus that of a normal distribution.
To explore the distribution of SUVmean and SUVmax a kernel density plot was used, which approximates the probability density of the variable; Kernel density plots have the advantage of being independent of the choice of origin, unlike histograms.
Figure 5 Kernel density plot over aggregate days in unemployment, 1998 2001.
We find that most all procedures yield a kernel density plot that matches the distribution of the original variables quite well.
For each gene, a kernel density plot for the expression index μgp and the difference dg was constructed.
The Kernel density plot showed clear bimodal ELISA S/P value distribution divided by the cut-off value.
Next, the empirical ρdiff distributions for each disease were graphed using kernel density plot.
Kernel density plot to show distribution of Total (blue) and Integrated (red) HIV-1-DNA at baseline.
The kernel density plot in Figure 1A was generated using the kde2d function from the MASS package in R (54).
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