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.
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In that case we use the powerful Central Limit Theorem which states, that if the sample size is large enough, then sampling distribution of the mean is approximately normally distributed with mean p and standard deviation [sigma]/[square root of n], where [mu] and [sigma] are population parameters, and n is the sample size.
Program CLT converted to MATLAB from the Central limit theorem on TI 83 calculator
We can give sufficient conditions under which the central limit theorem in Equation (3) holds for f.
Optimal Keno Strategies and the Central Limit Theorem.
Let us now illustrate the Central Limit Theorem in action.
To illustrate the concept of the buffer fund, assume that the limiting result implied by the central limit theorem applies exactly so we can write: (12) [Mathematical Expression Omitted] where [y.
Lee (1997), The central limit theorem for Euclidean minimal spanning trees I, Ann.
Central limit theorem for a class of random measures associated with germ-grain models.
n] satisfies a central limit theorem with mean and variance
Recall that there is a central limit theorem for the depth of nodes in (5) so that "most" nodes lie at [[mu].
He covers such topics as the binomial and Poisson distributions, the central limit theorem, normal distribution, the probability tree, and the Bayes theory using examples and computer simulation.
Finally, using the contraction method in continuous times established by Janson Neininger [16], we prove a central limit Theorem of N(x).
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