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  2. Charles Wilcoxon - Wikipedia

    en.wikipedia.org/wiki/Charles_Wilcoxon

    Charles Wilcoxon was born November 26, 1894, in Coshocton, Ohio, [1] or possibly Newark, Ohio. [2] He received his first drum in 1899 at the age of 5 and was taught basic music reading by his mother, a piano teacher. [3]

  3. Souders–Brown equation - Wikipedia

    en.wikipedia.org/wiki/Souders–Brown_equation

    The drum should have a vapor outlet at the top, liquid outlet at the bottom, and feed inlet at about the half-full level. At the vapor outlet, provide a de-entraining mesh pad within the drum such that the vapor must pass through that mesh before it can leave the drum.

  4. Lossless compression - Wikipedia

    en.wikipedia.org/wiki/Lossless_compression

    In fact, if we consider files of length N, if all files were equally probable, then for any lossless compression that reduces the size of some file, the expected length of a compressed file (averaged over all possible files of length N) must necessarily be greater than N. [citation needed] So if we know nothing about the properties of the data ...

  5. Median test - Wikipedia

    en.wikipedia.org/wiki/Median_test

    The Wilcoxon–Mann–Whitney U two-sample test or its generalisation for more samples, the Kruskal–Wallis test, can often be considered instead. The relevant aspect of the median test is that it only considers the position of each observation relative to the overall median, whereas the Wilcoxon–Mann–Whitney test takes the ranks of each ...

  6. William F. Ludwig Sr. - Wikipedia

    en.wikipedia.org/wiki/William_F._Ludwig_Sr.

    In 1955, Ludwig bought the name Ludwig back from Conn (WFL drums had been directly competing with Conn-made Ludwig drums) in order to found the Ludwig Drum Company. [10] [1] He published the book The Ludwig Drum and Bugle Manual in 1956, [13] followed by the Ludwig Tympani Instructor in 1957. [14] He also published The Ludwig Drum Method in ...

  7. Data compression - Wikipedia

    en.wikipedia.org/wiki/Data_compression

    Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive ...

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