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Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value. A boxplot is a standardized way of displaying the dataset based on the five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.
It is possible to quickly compare several sets of observations by comparing their five-number summaries, which can be represented graphically using a boxplot. In addition to the points themselves, many L-estimators can be computed from the five-number summary, including interquartile range, midhinge, range, mid-range, and trimean.
The box plot above, using the CLD methodology, is now far more informative. The cities are sorted in descending order from left to right. The color density is tiered with the cities having higher rainfall being colored with more dense or opaque tones; meanwhile, the cities with lower rainfall have less dense or more transparent tones.
For two continuous variables, a scatterplot is a common graph. When one variable is categorical and the other continuous, a box plot is common and when both are categorical a mosaic plot is common. These graphs are part of descriptive statistics .
Box-and-whisker plot with four mild outliers and one extreme outlier. In this chart, outliers are defined as mild above Q3 + 1.5 IQR and extreme above Q3 + 3 IQR. The interquartile range is often used to find outliers in data. Outliers here are defined as observations that fall below Q1 − 1.5 IQR or above Q3 + 1.5 IQR.
The Excel function QUARTILE.INC(array, quart) provides the desired quartile value for a given array of data, using Method 3 from above. The QUARTILE function is a legacy function from Excel 2007 or earlier, giving the same output of the function QUARTILE.INC.
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Using the medcouple, the whiskers of a boxplot can be adjusted for skew distributions and thus have a more accurate identification of outliers for non-symmetrical distributions. As a kind of order statistic , the medcouple belongs to the class of incomplete generalised L-statistics . [ 1 ]