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The **geometric mean** of a set of positive data is defined as the nth root of the product of all the members of the set, where n is the number of members.

## CalculationEdit

In a formula: the geometric mean of
*a _{1}*,

*a*, ...,

_{2}*a*is $ (a_1 \cdot a_2 \dotsb a_n)^{1/n} $, which is $ \sqrt[n]{a_1 \cdot a_2 \dotsb a_n} $.

_{n}The geometric mean of a data set is always smaller than or equal to the set's arithmetic mean (the two means are equal if and only if all members of the data set are equal). This allows the definition of the arithmetic-geometric mean, a mixture of the two which always lies in between.

The geometric mean is also the *arithmetic-harmonic mean* in the sense that if two sequences (*a*_{n}) and (*h*_{n}) are defined:

- $ a_{n+1} = \frac{a_n + h_n}{2}, \quad a_1=\frac{x + y}{2} $

and

- $ h_{n+1} = \frac{2}{\frac{1}{a_n} + \frac{1}{h_n}}, \quad h_1=\frac{2}{\frac{1}{x} + \frac{1}{y}} $

then *a*_{n} and *h*_{n} will converge to the geometric mean of *x* and *y*.

## Relationship with arithmetic mean of logarithmsEdit

The product form of the geometric mean computation is expressed as:

- $ \left(\prod_{i=1}^nx_i\right)^{1/n} $

By using logarithmic identities to transform the formula, we can express the multiplications as a sum and the power as a multiplication.

- $ \exp\left[\frac1n\sum_{i=1}^n\ln x_i\right] $.

This is simply computing the arithmetic mean of the logarithm transformed values of $ x_i $ (i.e. the arithmetic mean on the log scale) and then using the exponentiation to return the computation to the original scale. I.e., it is the generalised f-mean with f(x) = ln x.

Therefore the geometric mean is related to the log-normal distribution.
The log-normal distribution is a distribution which is normal for the logarithm
transformed values. We see that the
geometric mean is the exponentiated value of the mean of the log transformed
values, e.g. e^{mean(ln(X))}.

## When to use the Geometric MeanEdit

The geometric mean is useful to determine "average factors". For example, if a stock rose 10% in the first year, 20% in the second year and fell 15% in the third year, then we compute the geometric mean of the factors 1.10, 1.20 and 0.85 as (1.10 × 1.20 × 0.85)^{1/3} = 1.0391... and we conclude that the stock rose 3.91 percent per year, on average.

**Put another way...**

The arithmetic mean is relevant any time several quantities add together to produce a total. The arithmetic mean answers the question, "if all the quantities had the same value, what would that value have to be in order to achieve the same total?"

In the same way, the geometric mean is relevant any time several quantities multiply together to produce a product. The geometric mean answers the question, "if all the quantities had the same value, what would that value have to be in order to achieve the same product?"

For example, suppose you have an investment which earns 10% the first year, 50% the second year, and 30% the third year. What is its average rate of return? It is not the arithmetic mean, because what these numbers mean is that on the first year your investment was multiplied (not added to) by 1.10, on the second year it was multiplied by 1.50, and the third year it was multiplied by 1.30. The relevant quantity is the geometric mean of these three numbers. Source.

## See also Edit

- Arithmetic mean
- Arithmetic-geometric mean
- Average
- Generalized mean
- Geometric standard deviation
- Harmonic mean
- Hyperbolic coordinates
- Inequality of arithmetic and geometric means
- Log-normal distribution
- Muirhead's inequality
- Product
- Weighted geometric mean

## External links Edit

- Calculation of the geometric mean of two numbers in comparison to the arithmetic solution
- Arithmetic and geometric means at cut-the-knot
- When to use the geometric mean
- Geometric Mean on MathWorldcs:Geometrický průměr

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