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<record version="1" id="3618">
 <title>root-mean-square</title>
 <name>RootMeanSquare3</name>
 <created>2002-11-23 11:55:39</created>
 <modified>2002-11-23 11:55:39</modified>
 <type>Definition</type>
 <creator id="1001" name="pbruin"/>
 <author id="1001" name="pbruin"/>
 <classification>
	<category scheme="msc" code="26-00"/>
	<category scheme="msc" code="26D15"/>
 </classification>
 <synonyms>
	<synonym concept="root-mean-square" alias="root mean square"/>
	<synonym concept="root-mean-square" alias="rms"/>
	<synonym concept="root-mean-square" alias="quadratic mean"/>
 </synonyms>
 <related>
	<object name="ArithmeticMean"/>
	<object name="GeometricMean"/>
	<object name="HarmonicMean"/>
	<object name="PowerMean"/>
	<object name="WeightedPowerMean"/>
	<object name="ArithmeticGeometricMeansInequality"/>
	<object name="GeneralMeansInequality"/>
	<object name="ProofOfGeneralMeansInequality"/>
 </related>
 <keywords>
	<term>mean</term>
	<term>expectation</term>
 </keywords>
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 <content>If $x_1,x_2,\ldots,x_n$ are real numbers, we define their
\emph{root-mean-square} or \emph{quadratic mean} as
$$
R(x_1,x_2,\ldots,x_n)=\sqrt{\frac{x_1^2+x_2^2+\cdots+x_n^2}{n}}.
$$

The root-mean-square of a random variable X is defined as the square
root of the expectation of $X^2$:
$$
R(X)=\sqrt{E(X^2)}
$$

If $X_1,X_2,\ldots,X_n$ are random variables with standard deviations
$\sigma_1,\sigma_2,\ldots,\sigma_n$, then the standard deviation of
their arithmetic mean, $\frac{X_1+X_2+\cdots+X_n}{n}$, is the
root-mean-square of $\sigma_1,\sigma_2,\ldots,\sigma_n$.</content>
</record>
