In mathematics, an average, mean, or central tendency of a data set refers to a measure of the "middle" or "expected value" of the data set. An average is a single value that is meant to typify a list of values. It is calculated by combining the measurements related to a set and computing a number that typifies the set. If all the numbers in a list are the same, then this number should be used. However, if the numbers in a set are not all the same, special formulas are used to find the average or mean value.
- 1 Etymology
- 2 Terminology
- 3 Calculation
- 4 Types
- 5 Solutions to variational problems
- 6 Miscellaneous types
- 7 In data streams
- 8 See also
- 9 Notes
- 10 References
- 11 External links
- 12 Credits
The original meaning of the word average is "damage sustained at sea": the same word is found in Arabic as awar, in Italian as avaria, and in French as avarie. An "average adjuster" is a person who assesses an insurable loss.
Marine damage is either particular average, which is borne only by the owner of the damaged property, or general average, where the owner can claim a proportional contribution from all the parties to the marine venture. The type of calculations used in adjusting general average gave rise to the use of "average" to mean "arithmetic mean."
The most common form of an "average value" is known in mathematics as the arithmetic mean. In addition, there are other types of averages, such as geometric mean and harmonic mean, detailed below. The term median is used most often to describe house prices and income levels.
In statistics, the term central tendency is used in some fields of empirical research to refer to what statisticians sometimes call "location." There are many different descriptive statistics that can be chosen as a measurement of the central tendency of the data items.
Simply put, if numbers are given, each number denoted by ai, where , the arithmetic mean (AM) is the sum of the ai's divided by or
The arithmetic mean is often simply called the mean. For two numbers, such as 2 and 8, the arithmetic mean is obtained by finding a value A such that 2 + 8 = A + A. In such a case, one calculates the value of A as: A = (2 + 8)/2 = 5. Switching the order of 2 and 8 to read 8 and 2 does not change the resultant value for A. The mean 5 is not less than the minimum 2 nor greater than the maximum 8. If we increase the number of terms in the list for which we want an average, we get, for example, that the arithmetic mean of 2, 8, and 11 is found by solving for the value of A in the equation 2 + 8 + 11 = A + A + A. One gets: A = (2 + 8 + 11)/3 = 7.
Again, changing the order of the three members of the list does not change the result: A = (8 + 11 + 2)/3 = 7, and that 7 is between 2 and 11. This summation method is easily generalized for lists with any number of elements. However, the mean of a list of integers is not necessarily an integer. "The average family has 1.7 children" is a jarring way of making a statement that is more appropriately expressed by "the average number of children in the group of families surveyed is 1.7."
The geometric mean (GM) of is defined as
Geometric mean can be thought of as the antilog of the arithmetic mean of the logs of the numbers.
Example: Geometric mean of 2 and 8 is .
Harmonic mean for a set of numbers is defined as the reciprocal of the arithmetic mean of the reciprocals of 's:
It is useful, for example, when calculating the average speed of travel. For example, if the speed for going from point A to B was 60km/h, and the speed for returning from B to A was 40km/h, then the average speed is given by .
Inequality concerning AM, GM, and HM
A well-known inequality concerning Arithmetic, Geometric, and Harmonic means for any set of positive numbers is:
It is easy to remember this order, by noting that the alphabetical order of the letters A, G and H is preserved in the inequality.
Mode and median
The most frequently occurring number in a list of numbers is called the mode. The mode of the list (1, 2, 2, 3, 3, 3, 4) is 3. The mode is not necessarily well defined, the list (1, 2, 2, 3, 3, 5) has the two modes 2 and 3. The mode can be subsumed under the general method of defining averages by understanding it as taking the list and setting each member of the list equal to the most common value in the list if there is a most common value. This list is then equated to the resulting list with all values replaced by the same value. Since they are already all the same, this does not require any change.
To find the median, order the list according to its elements' magnitude and then repeatedly remove the pair consisting of the highest and lowest values until either one or two values are left. If exactly one value is left, it is the median; if two values, the median is the arithmetic mean of these two. This method takes the list 1, 7, 3, 13 and orders it to read 1, 3, 7, 13. Then the 1 and 13 are removed to obtain the list 3, 7. Since there are two elements in this remaining list, the median is their arithmetic mean, (3 + 7)/2 = 5. Now do the same for the equal-sized list consisting of all the same value M: M, M, M, M. It is already ordered. We remove the two end values to get M, M. We take their arithmetic mean to get M. Finally, set this result equal to our previous result to get M = 5.
The annualized return is a type of average used in finance. For example, if there are two years in which the return in the first year is −10% and the return in the second year is +60%, then the annualized return, R, can be obtained by solving the equation: (1 − 10%) × (1 + 60%) = (1 − 0.1) × (1 + 0.6) = (1 + R) × (1 + R). The value of R that makes this equation true is 0.2, or 20%. Note that changing the order to find the annualized returns of +60% and −10% gives the same result as the annualized returns of −10% and +60%.
This method can be generalized to examples in which the periods are not all of one-year duration. Annualization of a set of returns is a variation on the geometric average that provides the intensive property of a return per year corresponding to a list of returns. For example, consider a period of a half of a year for which the return is −23% and a period of two and one half years for which the return is +13%. The annualized return for the combined period is the single year return, R, that is the solution of the following equation: (1 − 0.23)0.5 × (1 + 0.13)2.5 = (1 + R)0.5+2.5, giving an annualized return R of 0.0600 or 6.00%.
The table of mathematical symbols explains the symbols used below.
|Name||Equation or description|
|Median||The middle value that separates the higher half from the lower half of the data set|
|Geometric median||A rotation invariant extension of the median for points in Rn|
|Mode||The most frequent value in the data set|
|Truncated mean||The arithmetic mean of data values after a certain number or proportion of the highest and lowest data values have been discarded|
|Interquartile mean||A special case of the truncated mean, using the interquartile range|
|Winsorized mean||Similar to the truncated mean, but, rather than deleting the extreme values, they are set equal to the largest and smallest values that remain|
Solutions to variational problems
Several measures of central tendency can be characterized as solving a variational problem, in the sense of the calculus of variations, namely minimizing variation from the center. That is, given a measure of statistical dispersion, one asks for a measure of central tendency that minimizes variation: such that variation from the center is minimal among all choices of center. In a quip, "dispersion precedes location." In the sense of spaces, the correspondence is:
|average absolute deviation||median|
Thus standard deviation about the mean is lower than standard deviation about any other point; the uniqueness of this characterization of mean and midrange follows from convex optimization, as the and norms are convex functions. Note that the median in this sense is not in general unique, and in fact any point between the two central points of a discrete distribution minimizes average absolute deviation. Similarly, the mode minimizes qualitative variation.
Additional, more sophisticated averages are: trimean, trimedian, and normalized mean.
One can create one's own average metric using generalized f-mean:
where f is any invertible function. The harmonic mean is an example of this using f(x) = 1/x, and the geometric mean is another, using f(x) = log x. Another example, expmean (exponential mean) is a mean using the function f(x) = ex, and it is inherently biased towards the higher values. However, this method for generating means is not general enough to capture all averages. A more general method for defining an average, y, takes any function of a list g(x1, x2, ..., xn), which is symmetric under permutation of the members of the list, and equates it to the same function with the value of the average replacing each member of the list: g(x1, x2, ..., xn) = g(y, y, ..., y). This most general definition still captures the important property of all averages that the average of a list of identical elements is that element itself. The function g(x1, x2, ..., xn) =x1+x2+ ...+ xn provides the arithmetic mean. The function g(x1, x2, ..., xn) =x1•x2• ...• xn provides the geometric mean. The function g(x1, x2, ..., xn) =x1−1+x2−1+ ...+ xn−1 provides the harmonic mean.
In data streams
The concept of an average can be applied to a stream of data as well as a bounded set, the goal being to find a value about which recent data is in some way clustered. The stream may be distributed in time, as in samples taken by some data acquisition system from which we want to remove noise, or in space, as in pixels in an image from which we want to extract some property. An easy-to-understand and widely used application of average to a stream is the simple moving average in which we compute the arithmetic mean of the most recent N data items in the stream. To advance one position in the stream, we add 1/N times the new data item and subtract 1/N times the data item N places back in the stream.
- For an axiomatic approach ,to averages, see: John Bibby (1974). Axiomatisations of the average and a further generalization of monotonic sequences. Glasgow Mathematical Journal 15:63–65.
- Coles, Stuart. 2001. An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. London, UK: Springer. ISBN 1852334592
- Hardy, G.H., J.E. Littlewood, and G. Pólya. 1988. Inequalities, 2nd ed. Cambridge, UK: Cambridge University Press. ISBN 978-0521358804
- Sahoo, Prasanna, and T. Riedel. 1998. Mean Value Theorems and Functional Equations. Singapore: World Scientific. ISBN 9810235445
- Williams, Edward, and Katie Prindle. 2006. Arithmetic the Easy Way. Hauppauge, NY: Barron's. ISBN 978-0764129131
All links retrieved May 3, 2016.
New World Encyclopedia writers and editors rewrote and completed the Wikipedia article in accordance with New World Encyclopedia standards. This article abides by terms of the Creative Commons CC-by-sa 3.0 License (CC-by-sa), which may be used and disseminated with proper attribution. Credit is due under the terms of this license that can reference both the New World Encyclopedia contributors and the selfless volunteer contributors of the Wikimedia Foundation. To cite this article click here for a list of acceptable citing formats.The history of earlier contributions by wikipedians is accessible to researchers here:
The history of this article since it was imported to New World Encyclopedia:
Note: Some restrictions may apply to use of individual images which are separately licensed.