# mean

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## mean,

in statistics, a type of averageaverage,
number used to represent or characterize a group of numbers. The most common type of average is the arithmetic mean. See median; mode.
. The arithmetic mean of a group of numbers is found by dividing their sum by the number of members in the group; e.g., the sum of the seven numbers 4, 5, 6, 9, 13, 14, and 19 is 70 so their mean is 70 divided by 7, or 10. Less often used is the geometric mean (for two quantities, the square root of their product; for n quantities, the nth root of their product).

## mean

see MEASURES OF CENTRAL TENDENCY.

## mean

[mēn]
(mathematics)
A single number that typifies a set of numbers, such as the arithmetic mean, the geometric mean, or the expected value. Also known as mean value.

## mean

Maths
a. the second and third terms of a proportion, as b and c in a/b = c/d
b. another name for average See also geometric mean

## MEAN

(MongoDB, Express, AngularJS, Node.js) A set of system software used for developing JavaScript-based Web applications. Node.js is server-side JavaScript, while Express provides an abbreviated framework for it. AngularJS is used for client-side JavaScript, and MongoDB is a highly scalable database that supports full clustering and automatic sharding. See MongoDB, Node.js, LAMP and database partitioning.
References in periodicals archive ?
The selection of a suitable initial mean vector is done using Umeans clustering in a heuristic way.
However, if the mean vector exists, it will be E(X) = [mu], and if the covariance matrix exists, it will be
The null hypothesis of this test is that the mean vector of model parameters estimated using aggregate soil loss information (either at the hydrological unit or county level) is not different from the "true" mean vector estimated for the model using onsite soil loss information (i.
p], [mu], [SIGMA]) is normally distributed with mean vector [?
Using the mean vector of the m new observations and formula 6 to obtain an estimate of the mean response for m new observations is also flawed.
2] fit of the model, where both the covariance matrix and the mean vector are invariant across the samples, is 2131.
There is currently debate as to the validity of transformations between linear variance and mean vector length (Fisher 1993).
It is also assumed that A is a realization from a multivariate normal distribution with mean vector [Mu] and covariance matrix [Sigma].
The GM model assumes a p-multinormal distribution, with mean vector [[Mu].

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