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CLTCross-Laminated Timber (engineered wood product)
CLTCommunity Land Trust
CLTConsolidação das Leis do Trabalho (Brazil)
CLTCentral Limit Theorem (probability theory)
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References in periodicals archive ?
18] Clemens Heuberger, Sara Kropf, and Stephan Wagner, Variances and covariances in the central limit theorem for the output of a transducer, European J.
By selecting other distributions from the provided list, the user will convince himself or herself that, for any distribution, the confidence level presents the percentage of confidence intervals which contain the population mean (of course, if the distribution is not normal, then the sample size should be large enough so that the Central Limit Theorem will take place).
The central limit theorem asserts that the PDF, P(y), of a variable y = ([xi]-m)/[sigma], where m = <[xi]> and [[sigma].
To understand the need for this concept, it is necessary to consider first the classical Central Limit Theorem which, under conditions that vary along different versions, allows for the distribution of the centered and standardized sample mean to converge to those corresponding to a normal standard distribution.
Kerov s central limit theorem for the Plancherel measure on Young diagrams.
Give an example of a situation where the Central Limit theorem may fail because of unmet assumptions.
The life and times of the central limit theorem, 2d ed.
Without any such evidence that the central limit theorem is applicable, the theorem does not provide any theoretical support for the log-normal distribution.
The formulation of an accompanying central limit theorem takes into account that there is no convenient notion of subtraction for convex bodies, and so the identification with support functions is used:
Note that the sum in the denominator above converges to 1/2 by the Central Limit Theorem.
Secondly, in terms of distribution, for a sufficiently large number of observations, the covariance's distribution will have a shape resembling to the normal distribution (for a sufficiently large number of observations the Central Limit Theorem (CLT) applies).
The main justification for this assumption is the Central Limit Theorem, which argues the distribution of a sum is asymptotically normal since economic data is determined as the sum of activities of independent agents.