Welcome to the root mean square calculator! Use it if you want to know how to calculate the RMS (A.K.A. quadratic mean) of any data set. But what is the root mean square? What is the root mean square formula? Scroll down - besides an RMS calculator, we also give the root mean square equation and explain how to calculate root mean square by hand!
What is root mean square? Root mean square formula
The root mean square formula for a set of n numbers x1, ..., xn is the following:
With the help of the summation sign we can rewrite it as:
We see that this expression is the square root of the arithmetic mean of the squares of the values in our set. This is where the name "root-mean-square", which is abbreviated as "RMS", comes from. Sometimes the root mean square is called the "quadratic mean".
How to calculate the root mean square by hand?
We now know what the root mean square is, so it's time to learn how to calculate RMS by hand.
2, 6, 3, -4, 2, 4, -1, 3, 2, -1 be our dataset for which we want to apply the root mean square equation to. We need to perform the following steps:
The first step is to find the square or each number. The set of squared values is the following:
4, 36, 9, 16, 4, 16, 1, 9, 4, 1.
Sum those squared numbers:
4 + 36 + 9 + 16 + 4 + 16 + 1 + 9 + 4 + 1 = 100.
100by the number of values contained in our dataset. We see that there are
10of them, so we obtain
100 / 10 = 10.
The last step we need to apply the root mean square equation is to compute the square root of the value obtained above. The quadratic mean of our dataset is
√10 ≈ 3.16
How to use this RMS calculator?
To use our root mean square calculator, start by entering your values. At first, eight fields are visible, but more fields will appear as you go. You can enter up to 30 numbers.
The root mean square of your numbers will be displayed at the bottom of the RMS calculator. It will update as you enter more and more variables.
Weighted root mean square
Similar to other means, the root mean square has a weighted variant. It is used when we want some values to contribute more to the average than others (in the default version each value contributes the same, i.e., they all have the same weight).
Suppose that we have a list of numbers
x₁, x₂, ..., xₙ, along with an associated list of weights
w₁, w₂, ..., wₙ.
The weighted root mean square is given by the formula:
Generalized (power) means
For all you more advanced readers, it may be useful for you to know that the quadratic mean (and most other means, like the arithmetic, geometric, and harmonic) are particular cases of a more general concept. Namely, we let
p ≠ 0 and define the generalized mean with the exponent
p of the values
x₁, x₂, ..., xₙ as:
p = 1, we have the arithmetic mean. By setting
p = 2, we obtain the quadratic mean, while
p = 3 would give the cubic mean, and so on. Also, note that for
p = -1, we arrive at the harmonic mean:
Finally, it can be proved that the geometric mean is the limit value when
It is true that the root mean square is less popular than the arithmetic or geometric mean, but it does have several important applications:
In statistics, the population standard deviation is just the root mean square of the differences between data points and the population expected value. In fact, the root mean square can be used to determine the standard deviation via the following formula:
xRMS² - (x̄)² = σ²
xRMSis the root mean square;
x̄is the population mean; and
σ²is the squared standard deviation (i.e., variance).
In physics, the RMS appears, e.g., in electrical engineering, signal processing, and the physics of gas molecules.
Let us just discuss one example.
Imagine we have
Nidentical particles with mass
m, and the
i-th particle is moving at speed
vi. We know that such a particle has the kinetic energy equal to
0.5 * m * vi2. In total, they have the kinetic energy of
0.5 * m * (v12 + ... + vn2).
If we want to find one speed which, with respect to all these particles, would give the same total kinetic energy, we quickly conclude that this speed would be the root mean square of all the speeds
v1, ..., vn.
To learn more about other applications of RMS in physics, you may want to check the: