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This theorem can be used to disprove the central limit theorem holds for by using proof by contradiction. This procedure involves proving that Lindeberg's condition fails for X k {\displaystyle X_{k}} .
An important example of a log-concave density is a function constant inside a given convex body and vanishing outside; it corresponds to the uniform distribution on the convex body, which explains the term "central limit theorem for convex bodies".
Animated examples of the CLT; General Dynamic SOCR CLT Activity; Interactive Simulation of the Central Limit Theorem for Windows; The SOCR CLT activity provides hands-on demonstration of the theory and applications of this limit theorem. A music video demonstrating the central limit theorem with a Galton board by Carl McTague
The characteristic function approach is particularly useful in analysis of linear combinations of independent random variables: a classical proof of the Central Limit Theorem uses characteristic functions and Lévy's continuity theorem. Another important application is to the theory of the decomposability of random variables.
Edgeworth's limit theorem ; Egorov's theorem (measure theory) Ehresmann's theorem (differential topology) Eilenberg–Zilber theorem (algebraic topology) Elitzur's theorem (quantum field theory, statistical field theory) Envelope theorem (calculus of variations) Equal incircles theorem (Euclidean geometry) Equidistribution theorem (ergodic theory)
Download as PDF; Printable version; ... The central limit theorem states that the distribution of an average of many ... is one of the solutions. For example, ...
The central limit theorem gives only an asymptotic distribution. As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails.
This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law). The log-normal distribution is the maximum entropy probability distribution for a random variate X —for which the mean and variance of ln( X ) are specified.