Root-Mean-Square

For a set of n numbers or values of a discrete distribution x_i, ..., x_n, the root-mean-square (abbreviated "RMS" and sometimes called the quadratic mean), is the square root of mean of the values x_i^2, namely

x_(RMS)=sqrt((x_1^2+x_2^2+...+x_n^2)/n)
(1)
=sqrt((sum_(i=1)^(n)x_i^2)/n)
(2)
=sqrt(<x^2>),
(3)

where <x^2> denotes the mean of the values x_i^2.

For a variate X from a continuous distribution P(x),

 x_(RMS)=sqrt((int[P(x)]^2dx)/(intP(x)dx)),
(4)

where the integrals are taken over the domain of the distribution. Similarly, for a function f(t) periodic over the interval [T_1,T_2], the root-mean-square is defined as

 f_(RMS)=sqrt(1/(T_2-T_1)int_(T_1)^(T_2)[f(t)]^2dt).
(5)

The root-mean-square is the special case M_2 of the power mean.

Hoehn and Niven (1985) show that

 R(a_1+c,a_2+c,...,a_n+c)<c+R(a_1,a_2,...,a_n)
(6)

for any positive constant c.

Physical scientists often use the term root-mean-square as a synonym for standard deviation when they refer to the square root of the mean squared deviation of a signal from a given baseline or fit.

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