central limit theorem

It is a special case of the central limit theorem.

In 1920 he published his first paper on the central limit theorem.

To apply the central limit theorem, one must use a large enough sample.

The best known and most important of these is known as the central limit theorem.

Any other distribution is expected to give the same result, as a consequence of the central limit theorem.

This article gives two concrete illustrations of the central limit theorem.

In the former case we have , which is related to the central limit theorem.

The central limit theorem may be applied to the distribution of the sample means.

It is sometimes called the functional central limit theorem.

The central limit theorem has a number of variants.

The central limit theorem supports the idea that this is a good approximation in many cases.

This is the central limit theorem as it applies to thermodynamic systems.

However, its importance derives mainly from the multivariate central limit theorem.

More broadly, the delta method may be considered a fairly general central limit theorem.

This confidence can actually be quantified by the central limit theorem and other mathematical results.

Such a distribution will be normal according to the central limit theorem except in pathological cases.

The central limit theorem can provide more detailed information about the behavior of than the law of large numbers.

This introduced the compound Poisson process and involved work on the central limit theorem.

Two representative mathematical results describing such patterns are the law of large numbers and the central limit theorem.

In other cases, the central limit theorem indicates that will be approximately normally distributed when is close to one.

Also, the distribution of the mean is known to be asymptotically normal due to the central limit theorem.

Because of the central limit theorem, many test statistics are approximately normally distributed for large samples.

This approximation is justified by the central limit theorem.

Another reason for random jitter to have a distribution like this is due to the central limit theorem.

A simplified formulation of the central limit theorem under strong mixing is: "