Difference between revisions of "Standard deviation"
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:<math>\sigma^2 = \operatorname{E}[(X-\operatorname{E}[X])^2] = \operatorname{E}[X^2] - (\operatorname{E}[X])^2</math> | :<math>\sigma^2 = \operatorname{E}[(X-\operatorname{E}[X])^2] = \operatorname{E}[X^2] - (\operatorname{E}[X])^2</math> | ||
| − | where the [[expectation|expected value]] of ''X'' is E(''X''). | + | where the [[expectation (math)|expected value]] of ''X'' is E(''X''). |
Thus the standard deviation is | Thus the standard deviation is | ||
Revision as of 14:54, January 19, 2009
| This article/section deals with mathematical concepts appropriate for late high school or early college. |
Standard deviation is a measure in statistics of the dispersion of a set of values (represented as ). It is defined as the square root of the variance of these values, where variance is defined as
where the expected value of X is E(X).
Thus the standard deviation is
The formula for standard deviation must not be confused with the formula
which is the formula for a point estimate of the true standard deviation from a sample size of n. As such this estimator itself has a variance which, as the formula indicates, decreases as the sample size increases.