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  2. Twelfth root of two - Wikipedia

    en.wikipedia.org/wiki/Twelfth_root_of_two

    The just or Pythagorean perfect fifth is 3/2, and the difference between the equal tempered perfect fifth and the just is a grad, the twelfth root of the Pythagorean comma (12 √ 531441/524288). The equal tempered Bohlen–Pierce scale uses the interval of the thirteenth root of three ( 13 √ 3 ).

  3. Our tech writer's pick for best budget earbuds are 50% off ...

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    When reviewing these headphones, Broida flagged that the Baseus "case can recharge the earbuds more than 16 times before needing to be recharged itself. And the earbuds last up to eight hours ...

  4. Square root of 2 - Wikipedia

    en.wikipedia.org/wiki/Square_root_of_2

    Technically, it should be called the principal square root of 2, to distinguish it from the negative number with the same property. Geometrically, the square root of 2 is the length of a diagonal across a square with sides of one unit of length; this follows from the Pythagorean theorem. It was probably the first number known to be irrational. [1]

  5. nth root - Wikipedia

    en.wikipedia.org/wiki/Nth_root

    A root of degree 2 is called a square root and a root of degree 3, a cube root. Roots of higher degree are referred by using ordinal numbers, as in fourth root, twentieth root, etc. The computation of an n th root is a root extraction. For example, 3 is a square root of 9, since 3 2 = 9, and −3 is also a square root of 9, since (−3) 2 = 9.

  6. Square root of a 2 by 2 matrix - Wikipedia

    en.wikipedia.org/wiki/Square_root_of_a_2_by_2_matrix

    Square roots that are not the all-zeros matrix come in pairs: if R is a square root of M, then −R is also a square root of M, since (−R)(−R) = (−1)(−1)(RR) = R 2 = M. A 2×2 matrix with two distinct nonzero eigenvalues has four square roots. A positive-definite matrix has precisely one positive-definite square root.

  7. Standard deviation - Wikipedia

    en.wikipedia.org/wiki/Standard_deviation

    A little algebra shows that the distance between P and M (which is the same as the orthogonal distance between P and the line L) (¯) is equal to the standard deviation of the vector (x 1, x 2, x 3), multiplied by the square root of the number of dimensions of the vector (3 in this case).

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