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  2. Scott's rule - Wikipedia

    en.wikipedia.org/wiki/Scott's_Rule

    Scott's rule. (Redirected from 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]

  3. Histogram - Wikipedia

    en.wikipedia.org/wiki/Histogram

    Sturges's formula implicitly bases bin sizes on the range of the data, and can perform poorly if n < 30, because the number of bins will be small—less than seven—and unlikely to show trends in the data well. On the other extreme, Sturges's formula may overestimate bin width for very large datasets, resulting in oversmoothed histograms. [14]

  4. Bin packing problem - Wikipedia

    en.wikipedia.org/wiki/Bin_packing_problem

    The bin packing problem[1][2][3][4] is an optimization problem, in which items of different sizes must be packed into a finite number of bins or containers, each of a fixed given capacity, in a way that minimizes the number of bins used. The problem has many applications, such as filling up containers, loading trucks with weight capacity ...

  5. Freedman–Diaconis rule - Wikipedia

    en.wikipedia.org/wiki/Freedman–Diaconis_rule

    Freedman–Diaconis rule. In statistics, the Freedman–Diaconis rule can be used to select the width of the bins to be used in a histogram. [1] It is named after David A. Freedman and Persi Diaconis. For a set of empirical measurements sampled from some probability distribution, the Freedman–Diaconis rule is designed approximately minimize ...

  6. Sturges's rule - Wikipedia

    en.wikipedia.org/wiki/Sturges's_rule

    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]

  7. Balls into bins problem - Wikipedia

    en.wikipedia.org/wiki/Balls_into_bins_problem

    The balls into bins (or balanced allocations) problem is a classic problem in probability theory that has many applications in computer science. The problem involves m balls and n boxes (or "bins"). Each time, a single ball is placed into one of the bins. After all balls are in the bins, we look at the number of balls in each bin; we call this ...

  8. First-fit bin packing - Wikipedia

    en.wikipedia.org/wiki/First-fit_bin_packing

    First-fit (FF) is an online algorithm for bin packing. Its input is a list of items of different sizes. Its output is a packing - a partition of the items into bins of fixed capacity, such that the sum of sizes of items in each bin is at most the capacity. Ideally, we would like to use as few bins as possible, but minimizing the number of bins ...

  9. Combination - Wikipedia

    en.wikipedia.org/wiki/Combination

    where n is the number of items, m is the number of bins, and is the number of items that go into bin i. One way to see why this equation holds is to first number the objects arbitrarily from 1 to n and put the objects with numbers ,, …, into the first bin in order, the objects with numbers +, +, …, into the

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