Capital risk and Central limit theorem: Difference between pages
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imported>Michele Allman-Ward (Created page with "Capital risk: the risk that all or part of the principal may be lost Category:Cash_and_Liquidity_Management") |
imported>Doug Williamson (Identify context as financial maths & link with Normal distribution page.) |
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''Financial maths.'' | |||
[[Category: | The central limit theorem states formally that the average of a large number of independent identically distributed random variables will have a normal distribution. | ||
The central limit theorem is important in sampling theory. It explains that sample means follow a normal distribution - regardless of the actual distribution of the parent population - and that the sample mean is an unbiased estimate of the parent population mean. | |||
The central limit theorem also explains why larger samples will - on average - produce better estimates of the parent population mean. | |||
The central limit theorem is sometimes known as the '' law of large numbers''. | |||
== See also == | |||
* [[Mean]] | |||
* [[Normal distribution]] | |||
* [[Sample]] | |||
* [[Sampling]] | |||
[[Category:Identify_and_assess_risks]] |
Revision as of 20:05, 2 September 2018
Financial maths.
The central limit theorem states formally that the average of a large number of independent identically distributed random variables will have a normal distribution.
The central limit theorem is important in sampling theory. It explains that sample means follow a normal distribution - regardless of the actual distribution of the parent population - and that the sample mean is an unbiased estimate of the parent population mean.
The central limit theorem also explains why larger samples will - on average - produce better estimates of the parent population mean.
The central limit theorem is sometimes known as the law of large numbers.