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In so doing, we obtain a SDQNR for each sensor and each independent trial and so Fig. 4 shows the distribution of SDQNRs of the quantized consensus states for a range of q-bit uniform quantizers using a box plot.
Figure 2 illustrates this using a box plot of Pearson's correlation coefficients between blood glucose and (A) K i, and (B) MRGluc for all 1,192 scans from the 112 studies.
We compared the statistical data between the three groups using a box plot.
Finally, we present our results on both percentage error (PE) and model separation (ΔTS) using a box plot representation.
For example, as our first step, we first plot the peptide abundances observed in the experiment for each LC-MS run using a box plot.
Peptide abundance data for each example has been displayed versus a LC-MS run order identification number (not true LC run order) using a box plot.
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This paper thus uses a box plot to analyze data features and proposes a new formula for the background values in the gray model to improve forecasting accuracy.
To highlight the distribution of m among the three classes of sequences, the rate of nucleotide misincorporation for each of them was represented using a box-plot.
In brief, the data for each spot match set were analyzed using a box-plot test in order to eliminate outliers, a K-S test to check normal distribution, an F test to analyze the variance, and finally Student's t test to compare the mean values, which were considered significant when p<0.05.
Subject strength variation was evaluated using a box-plot.
The whole data set was tested using a box-plot technique in order to analyze extreme values seperately.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com