central limit theorem

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cen·tral lim·it the·o·rem

the sum (or average) of n realizations of the same process, provided only that it has a finite variance, will approach the gaussian distribution as n becomes indefinitely large. This theory provides a broad warrant for the use of normal theory even for nongaussian data. In the form stated here, it constitutes the classical version; more general versions allow serious relaxation of the usual assumptions.
References in periodicals archive ?
We note that although the derivation here is based on the approach to the Gaussian distribution on the basis of the central limit theorem, the same type Edgeworth expansion can also be found in the context of nonlinear evolution of density fluctuations starting from random Gaussian linear fluctuations.
The presentation emphasizes both the relevance of the Functional Central Limit Theorem to the discussion as well as the econometric considerations behind novel approaches.
Kerov's central limit theorem for Schur-Weyl measures of parameter [alpha] = 1/2.
The Central Limit Theorem is a centerpiece of probability theory which also carries over to statistics.
In fact, it seems to us that, if the central limit theorem argument for log normality has any credence, it would more likely apply to the apical response than to the threshold dose defined from the apical response.
In fact, using the Central Limit Theorem, it is asymptotically 1/2 for both r and n - r large.
In order to have a situation which to be fitted for the Central Limit Theorem, as well as for the Law of Large Numbers, we have to consider a sufficiently large number of observations.
Evaluation of an interactive tutorial for teaching the central limit theorem.
In this paper MATLAB is used in a demonstration of the Central Limit Theorem (CLT).
Under some regularity conditions, the following central limit theorem holds for a spatial process
Many applications of the central limit theorem only demonstrate the Mandelbrot-Levy character.
The central limit theorem tells us that, in the long run, that collection of data can be reduced to a mean value and a spread or distribution.