During the analysis of power output characteristic, the width of the bin interval and the number of the data points in the interval determine the representation of the data, which are the basis of curve fitting of power characteristic.
Often this will be a single bin unless a significant interval of the respiratory cycle is spent in a given position, such as expiration.
We visually identified peaks in the spectra and divided them manually into bins (integrals); compared to equal-interval binning of the entire spectrum, this has the effect of reducing the total number of variables, aligning each bin more closely with an individual resonance, and excluding spectral regions that contain only noise across all samples.
The confidence interval of the QTL for yield at bin 8.05 overlapped with the confidence interval of a QTL for stalk tunnel length and relative stalk tunnel length.
The confidence interval of the QTL spanned ~700-kb and contained three bins, but these results demonstrate the high mapping resolution of the bin-map in combination with large population size.
The bins are referred to and plotted in this study by the highest frequency included (e.g., the 3-Hz bin refers to the averaged values of the following bin intervals: 2.00 2.25, 2.25 2.50, 2.50 2.75 and 2.75 3.00 Hz).
To investigate the possible geomagnetic activity effect, we collected all the calculated values into samples corresponding to different intervals (bins) of the AE index.
To obtain the detailed distribution of clustering coefficients, we separated the clustering coefficients into different bins with an interval of 0.1 and calculated the proportion of the proteins in each bin.
We plotted the median of these bins, and used a bootstrapped estimate of the median (MATLAB function bootci) to find the 95% confidence intervals of the bin median.
This is because the shape of the empirical skewed histogram and the node degrees can be quite sensitive to the number of binning intervals and distribution of binning cut-off values.
