[Answer] What is the square root of the variance?

Answer: simply the standard deviation squared s2=s x s

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What is the square root of the variance? Sigma is the eighteenth letter of the Greek alphabet. In the system of Greek numerals it has a value of 200. When used at the end of a word the final form is used e.g. Ὀδυσσεύς; note the two sigmas in the center of the name and the word-final sigma at the end. Variance of X | STAT 414 / 415 Variance - investopedia.com Let X be a random variable with mean value μ: ${\displaystyle \operatorname {E} [X]=\mu .\ \!}$ Here the operator E denotes the average or expected value of X. Then the standard deviation of X is the quantity ${\displaystyle {\begin{aligned}\sigma &={\sqrt {\operatorname {E} \left[(X-\mu )^{2}\right]}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]+\operatorname {E} [-2\mu X]+\operatorname {E} \left[\mu ^… Let X be a random variable with mean value μ: ${\displaystyle \operatorname {E} [X]=\mu .\ \!}$ Here the operator E denotes the average or expected value of X. Then the standard deviation of X is the quantity ${\displaystyle {\begin{aligned}\sigma &={\sqrt {\operatorname {E} \left[(X-\mu )^{2}\right]}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]+\operatorname {E} [-2\mu X]+\operatorname {E} \left[\mu ^{2}\right]}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]-2\mu \operatorname {E} [X]+\mu ^{2}}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]-2\mu ^{2}+\mu ^{2}}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]-\mu ^{2}}}\\&={\sqrt {\operatorname {E} \left[X^{2}\right]-(\operatorname {E} [X])^{2}}}\end{aligned}}}$ (derived using the properties of expected value). In other words the standard deviation σ (sigma) is the square root of the variance of X; i.e. it is the square root of the average value of (X − μ) . The standard deviation of a (univariate) probability distribution is the same as that of a random variable having that distribution. Not all random variables have a standard deviation since these expected values need not exist. For example the standard deviation of a random variable that follows a Cauchy distribution is undefined because its expected value μ is undefined. In the case where X takes random values from a finite data set x1 x2 ... xN with each value having the same probability the standard deviation is ${\displaystyle \sigma ={\sqrt {{\frac {1}{N}}\left[(x_{1}-\mu )^{2}+(x_{2}-\mu )^{2}+\cdots +(x_{N}-\mu )^{2}\right]}} {\text{...

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