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.)
 
Line 1: Line 1:
Capital risk: the risk that all or part of the principal may be lost
''Financial maths.''


[[Category:Cash_and_Liquidity_Management]]
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.


See also