Geometric mean
In a formula: the geometric mean of
a1, a2, ..., an is
, which is
.
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.
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 (an) and (hn) are defined:
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Relationship with arithmetic mean of logarithms
The product form of the geometric mean computation is expressed as:
.
(i.e. the arithmetic mean in log space) and then using the exponentiation to return the computation to real space. 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. emean(ln(X)).
See also
- 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
- Calculation of the geometric mean of two numbers in comparison to the arithmetic solution
- Arithmetic and geometric means
