Standard box plots were created using MATLAB with the horizontal line within each box indicating the sample median, the bottom and top of the box representing the first and third quartile, respectively, and the whiskers extending to the most extreme data points that fall within 1.5 times the difference between the first and third quartile.
On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually with circles.
In each box, the central mark denotes the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered to be outliers, and outliers are plotted individually using the symbol +.
Standard MATLAB boxplots were used to plot class distributions (Fig. 6B,C; supplementary material Fig. S5B D): the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considering outliers, and outliers are plotted individually (red crosses).
In box and whisker plots (C, D ), the central red line is the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend to the most extreme data points.
The bold bar inside the box represents the median, while dashed lines reach to the most extreme data points, that are no more than 1.5 times the interquartile range away from the upper or lower box edge.
Within figures, the central line in box-plots is the median, the box is defined by 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually (∼ ±2.7σ).
In each box the central mark is the median; edges are the 25th and 75th percentiles; whiskers extend to the most extreme data points not considered outliers; and outliers are plotted individually.
The black vertical lines extend to the most extreme data points not considered outliers.
The use of robust regression methods allows reduction of the contribution of extreme data points in a data set.
