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  2. Larson–Miller relation - Wikipedia

    en.wikipedia.org/wiki/LarsonMiller_relation

    F.R. Larson and J. Miller proposed that creep rate could adequately be described by the Arrhenius type equation: = / Where r is the creep process rate, A is a constant, R is the universal gas constant, T is the absolute temperature, and is the activation energy for the creep process.

  3. Hollomon–Jaffe parameter - Wikipedia

    en.wikipedia.org/wiki/Hollomon–Jaffe_parameter

    The Hollomon–Jaffe parameter (HP), also generally known as the LarsonMiller parameter, [1] describes the effect of a heat treatment at a temperature for a certain time. [2] This parameter is especially used to describe the tempering of steels, so that it is also called tempering parameter.

  4. Talk:Larson–Miller relation - Wikipedia

    en.wikipedia.org/wiki/Talk:LarsonMiller_relation

    In the equation shown on this page, it shows the larson-miller parameter to be the activation energy over the gas constant and then has log of the time on the other side. Up until this point natural log was being used, in that case it should be the natural log of time.

  5. Ratio estimator - Wikipedia

    en.wikipedia.org/wiki/Ratio_estimator

    The ratio estimator is a statistical estimator for the ratio of means of two random variables. Ratio estimates are biased and corrections must be made when they are used in experimental or survey work. The ratio estimates are asymmetrical and symmetrical tests such as the t test should not be used to generate confidence intervals.

  6. Miller index - Wikipedia

    en.wikipedia.org/wiki/Miller_index

    Miller–Bravais indices. With hexagonal and rhombohedral lattice systems, it is possible to use the Bravais–Miller system, which uses four indices (h k i ℓ) that obey the constraint h + k + i = 0. Here h, k and ℓ are identical to the corresponding Miller indices, and i is a redundant index.

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  8. Mills ratio - Wikipedia

    en.wikipedia.org/wiki/Mills_ratio

    The inverse Mills ratio is the ratio of the probability density function to the complementary cumulative distribution function of a distribution. Its use is often motivated by the following property of the truncated normal distribution. If X is a random variable having a normal distribution with mean μ and variance σ 2, then

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