Difference between revisions of "Standard deviation"

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which is the formula for a [[point estimate]] of the true standard deviation from a sample size of ''n''.  As such this [[statistical estimator]] itself has a variance which, as the formula indicates, decreases as the sample size increases.
 
which is the formula for a [[point estimate]] of the true standard deviation from a sample size of ''n''.  As such this [[statistical estimator]] itself has a variance which, as the formula indicates, decreases as the sample size increases.
 
It may be important to consider the standard deviation is but ''a'' measure of dispersion, and not the only one.  For instance, a [[Cauchy distribution]] has an undefined standard deviation (or mean), yet a glance at its [[Probability density function | pdf]] suggests that it is not "infinitely" dispersed.
 
  
  

Revision as of 17:15, November 14, 2009

<math>\frac{d}{dx} \sin x=?\,</math> 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 <math>X</math>). It is defined as the square root of the variance of these values, where variance is defined as

<math>\sigma^2 = \operatorname{E}[(X-\operatorname{E}[X])^2] = \operatorname{E}[X^2] - (\operatorname{E}[X])^2</math>

where the expected value of X is E(X).

Thus the standard deviation is

<math>\sigma = \sqrt{\operatorname{E}[(X-\operatorname{E}[X])^2]} = \sqrt{\operatorname{E}[X^2] - (\operatorname{E}[X])^2}</math>

The formula for standard deviation must not be confused with the formula

<math>S_{n} = \sqrt {\sum_n(X_n - \bar X)^2 \over n - 1}</math>

(where <math>\bar X = {\sum_n X_n \over N}</math> is the sample mean).

which is the formula for a point estimate of the true standard deviation from a sample size of n. As such this statistical estimator itself has a variance which, as the formula indicates, decreases as the sample size increases.