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  2. Central moment - Wikipedia

    en.wikipedia.org/wiki/Central_moment

    The first central moment μ 1 is 0 (not to be confused with the first raw moment or the expected value μ). The second central moment μ 2 is called the variance, and is usually denoted σ 2, where σ represents the standard deviation. The third and fourth central moments are used to define the standardized moments which are used to define ...

  3. Moment (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Moment_(mathematics)

    The fourth central moment is a measure of the heaviness of the tail of the distribution. Since it is the expectation of a fourth power, the fourth central moment, where defined, is always nonnegative; and except for a point distribution, it is always strictly positive. The fourth central moment of a normal distribution is 3σ 4.

  4. Image moment - Wikipedia

    en.wikipedia.org/wiki/Image_moment

    The term invariant moments is often abused in this context. However, while moment invariants are invariants that are formed from moments, the only moments that are invariants themselves are the central moments. [citation needed] Note that the invariants detailed below are exactly invariant only in the continuous domain.

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    For any non-negative integer , the plain central moments are: [23] ⁡ [()] = {()!! Here !! denotes the double factorial, that is, the product of all numbers from to 1 that have the same parity as . The central absolute moments coincide with plain moments for all even orders, but are nonzero for odd orders.

  6. Standardized moment - Wikipedia

    en.wikipedia.org/wiki/Standardized_moment

    In probability theory and statistics, a standardized moment of a probability distribution is a moment (often a higher degree central moment) that is normalized, typically by a power of the standard deviation, rendering the moment scale invariant. The shape of different probability distributions can be compared using standardized moments. [1]

  7. Cumulant - Wikipedia

    en.wikipedia.org/wiki/Cumulant

    The first cumulant is the expected value; the second and third cumulants are respectively the second and third central moments (the second central moment is the variance); but the higher cumulants are neither moments nor central moments, but rather more complicated polynomial functions of the moments.

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    www.aol.com/games/play/masque-publishing/whist

    Play the classic trick-taking card game. Lead with your strongest suit and work with your partner to get 2 points per hand.

  9. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    The kurtosis is the fourth standardized moment, defined as ⁡ [] = ⁡ [()] = ⁡ [()] (⁡ [()]) =, where μ 4 is the fourth central moment and σ is the standard deviation.Several letters are used in the literature to denote the kurtosis.