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  2. Sturges's rule - Wikipedia

    en.wikipedia.org/wiki/Sturges's_rule

    Sturges's rule [1] is a method to choose the number of bins for a histogram. Given observations, Sturges's rule suggests using ^ = + ⁡ bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method. [3]

  3. Scott's rule - Wikipedia

    en.wikipedia.org/wiki/Scott's_Rule

    Scott's rule is a method to select the number of bins in a histogram. [1] Scott's rule is widely employed in data analysis software including R, [2] Python [3] and Microsoft Excel where it is the default bin selection method. [4]

  4. Histogram - Wikipedia

    en.wikipedia.org/wiki/Histogram

    A histogram is a visual representation of the distribution of quantitative data. To construct a histogram, the first step is to "bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval.

  5. Data binning - Wikipedia

    en.wikipedia.org/wiki/Data_binning

    Data binning, also called data discrete binning or data bucketing, is a data pre-processing technique used to reduce the effects of minor observation errors.The original data values which fall into a given small interval, a bin, are replaced by a value representative of that interval, often a central value (mean or median).

  6. Frequency (statistics) - Wikipedia

    en.wikipedia.org/wiki/Frequency_(statistics)

    A histogram is a representation of tabulated frequencies, shown as adjacent rectangles or squares (in some of situations), erected over discrete intervals (bins), with an area proportional to the frequency of the observations in the interval. The height of a rectangle is also equal to the frequency density of the interval, i.e., the frequency ...

  7. Freedman–Diaconis rule - Wikipedia

    en.wikipedia.org/wiki/Freedman–Diaconis_rule

    10000 samples from a normal distribution data binned using different rules. The Freedman-Diaconis rule results in 61 bins, the Scott rule 48 and Sturges' rule 15. With the factor 2 replaced by approximately 2.59, the Freedman–Diaconis rule asymptotically matches Scott's Rule for data sampled from a normal distribution.

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  9. Bin (computational geometry) - Wikipedia

    en.wikipedia.org/wiki/Bin_(computational_geometry)

    The size of a candidate's array is the number of bins it intersects. For example, in the top figure, candidate B has 6 elements arranged in a 3 row by 2 column array because it intersects 6 bins in such an arrangement. Each bin contains the head of a singly linked list. If a candidate intersects a bin, it is chained to the bin's linked list.