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The most prominent example of a mesokurtic distribution is the normal distribution family, regardless of the values of its parameters. A few other well-known distributions can be mesokurtic, depending on parameter values: for example, the binomial distribution is mesokurtic for p = 1 / 2 ± 1 / 12 {\textstyle p=1/2\pm {\sqrt {1/12}}} .
The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include: mounded (or unimodal), U-shaped, J-shaped, reverse-J shaped and multi-modal. [1] A bimodal distribution would have two high points rather than one. The shape of a distribution is ...
The uniform distribution or rectangular distribution on [a,b], where all points in a finite interval are equally likely, is a special case of the four-parameter Beta distribution. The Irwin–Hall distribution is the distribution of the sum of n independent random variables, each of which having the uniform distribution on [0,1].
Weight distribution is the apportioning of weight within a vehicle, especially cars, airplanes, and trains. Typically, it is written in the form x/y, where x is the ...
The weight distribution is the sequence of numbers = # {() =} giving the number of codewords c in C having weight t as t ranges from 0 to n. The weight enumerator is the bivariate polynomial (;,) = =.
The normal distribution is NOT assumed nor required in the calculation of control limits. Thus making the IndX/mR chart a very robust tool. This is demonstrated by Wheeler using real-world data [4], [5] and for a number of highly non-normal probability distributions.
The PERT distribution assigns very small probability to extreme values, particularly to the extreme furthest away from the most likely value if the distribution is strongly skewed. [ 6 ] [ 7 ] The Modified PERT distribution [ 8 ] was proposed to provide more control on how much probability is assigned to tail values of the distribution.
The median is 3 and the weighted median is the element corresponding to the weight 0.3, which is 4. The weights on each side of the pivot add up to 0.45 and 0.25, satisfying the general condition that each side be as even as possible. Any other weight would result in a greater difference between each side of the pivot.
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