In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation. Show The arithmetic mean is often known simply as the mean. It is an average, a measure of the centre of a set of data. The arithmetic mean is calculated by adding up all the values and dividing the sum by the total number of values. For example, the mean of \(7\), \(4\), \(5\) and \(8\) is \(\frac{7+4+5+8}{4}=6\). If the data values are \(x_1\), \(x_2\), …, \(x_n\), then we have \(\bar{x}=\frac{1}{n}\sum_{i=1}^n x_i\), where \(\bar{x}\) is a symbol representing the mean of the \(x_i\) values. This rearranges to give the useful result \[n\bar x = \sum_{i=1}^n x_i,\] that is, the arithmetic mean is the number \(\bar x\) for which having \(n\) copies of this number gives the same sum as the original data. So the sum of a set of numbers in some sense “averages” them. If the data are grouped, with \(f_i\) occurrences of the value \(x_i\) for \(i=1\), \(2\), …, \(n\), then their mean is given by \[\bar{x}=\frac{\sum_{i=1}^n f_ix_i}{\sum_{i=1}^n f_i},\] where the numerator is the sum of all of the \(x_i\) values and the denominator is the total number of values. The arithmetic mean is sensitive to outlier values. The mean value of a function \(f(x)\) over the interval \(a\le x\le b\) is likewise the value \(M\) for which the constant function \(f(x)=M\) has the same “sum” as the original function. The “sum” of a function over an interval is the integral of the function, as shown in this sketch: Thus the mean \(M\) is given by \(M(b-a)=\int_a^b f(x)\,dx\), so \[M=\frac{\int_a^b f(x)\,dx}{b-a}.\] The integral therefore “averages” the function. In mathematics and statistics, the arithmetic mean ( /ˌærɪθˈmɛtɪk ˈmiːn/ air-ith-MET-ik) or arithmetic average, or just the mean or the average (when the context is clear), is the sum of a collection of numbers divided by the count of numbers in the collection.[1] The collection is often a set of results of an experiment or an observational study, or frequently a set of results from a survey. The term "arithmetic mean" is preferred in some contexts in mathematics and statistics, because it helps distinguish it from other means, such as the geometric mean and the harmonic mean. In addition to mathematics and statistics, the arithmetic mean is used frequently in many diverse fields such as economics, anthropology and history, and it is used in almost every academic field to some extent. For example, per capita income is the arithmetic average income of a nation's population. While the arithmetic mean is often used to report central tendencies, it is not a robust statistic, meaning that it is greatly influenced by outliers (values that are very much larger or smaller than most of the values). For skewed distributions, such as the distribution of income for which a few people's incomes are substantially greater than most people's, the arithmetic mean may not coincide with one's notion of "middle", and robust statistics, such as the median, may provide better description of central tendency. DefinitionGiven a data set X = { x 1 , … , x n } {\displaystyle X=\{x_{1},\ldots ,x_{n}\}} , the arithmetic mean (or mean or average), denoted x ¯ {\displaystyle {\bar {x}}} (read x {\displaystyle x} bar), is the mean of the n {\displaystyle n} values x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} .[2] The arithmetic mean is the most commonly used and readily understood measure of central tendency in a data set. In statistics, the term average refers to any of the measures of central tendency. The arithmetic mean of a set of observed data is defined as being equal to the sum of the numerical values of each and every observation, divided by the total number of observations. Symbolically, if we have a data set consisting of the values a 1 , a 2 , … , a n {\displaystyle a_{1},a_{2},\ldots ,a_{n}} , then the arithmetic mean A {\displaystyle A} is defined by the formula: A = 1 n ∑ i = 1 n a i = a 1 + a 2 + ⋯ + a n n {\displaystyle A={\frac {1}{n}}\sum _{i=1}^{n}a_{i}={\frac {a_{1}+a_{2}+\cdots +a_{n}}{n}}} [3](for an explanation of the summation operator, see summation.) For example, if the monthly salaries of 10 employees of a firm are: 2500, 2700, 2400, 2300, 2550, 2650, 2750, 2450, 2600, 2400, then the arithmetic mean is 2500 + 2700 + 2400 + 2300 + 2550 + 2650 + 2750 + 2450 + 2600 + 2400 10 = 2530. {\displaystyle {\frac {2500+2700+2400+2300+2550+2650+2750+2450+2600+2400}{10}}=2530.}If the data set is a statistical population (i.e., consists of every possible observation and not just a subset of them), then the mean of that population is called the population mean, and denoted by the Greek letter μ {\displaystyle \mu } . If the data set is a statistical sample (a subset of the population), it is called the sample mean (which for a data set X {\displaystyle X} is denoted as X ¯ {\displaystyle {\overline {X}}} ). The arithmetic mean can be similarly defined for vectors in multiple dimension, not only scalar values; this is often referred to as a centroid. More generally, because the arithmetic mean is a convex combination (coefficients sum to 1), it can be defined on a convex space, not only a vector space. Motivating propertiesThe arithmetic mean has several properties that make it useful, especially as a measure of central tendency. These include:
Additional properties
Contrast with medianThe arithmetic mean may be contrasted with the median. The median is defined such that no more than half the values are larger than, and no more than half are smaller than, the median. If elements in the data increase arithmetically, when placed in some order, then the median and arithmetic average are equal. For example, consider the data sample 1 , 2 , 3 , 4 {\displaystyle {1,2,3,4}} . The average is 2.5 {\displaystyle 2.5} , as is the median. However, when we consider a sample that cannot be arranged so as to increase arithmetically, such as 1 , 2 , 4 , 8 , 16 {\displaystyle {1,2,4,8,16}} , the median and arithmetic average can differ significantly. In this case, the arithmetic average is 6.2, while the median is 4. In general, the average value can vary significantly from most values in the sample, and can be larger or smaller than most of them. There are applications of this phenomenon in many fields. For example, since the 1980s, the median income in the United States has increased more slowly than the arithmetic average of income.[4] GeneralizationsWeighted averageA weighted average, or weighted mean, is an average in which some data points count more heavily than others, in that they are given more weight in the calculation.[5] For example, the arithmetic mean of 3 {\displaystyle 3} and 5 {\displaystyle 5} is ( 3 + 5 ) 2 = 4 {\displaystyle {\frac {(3+5)}{2}}=4} , or equivalently ( 1 2 ⋅ 3 ) + ( 1 2 ⋅ 5 ) = 4 {\displaystyle \left({\frac {1}{2}}\cdot 3\right)+\left({\frac {1}{2}}\cdot 5\right)=4} . In contrast, a weighted mean in which the first number receives, for example, twice as much weight as the second (perhaps because it is assumed to appear twice as often in the general population from which these numbers were sampled) would be calculated as ( 2 3 ⋅ 3 ) + ( 1 3 ⋅ 5 ) = 11 3 {\displaystyle \left({\frac {2}{3}}\cdot 3\right)+\left({\frac {1}{3}}\cdot 5\right)={\frac {11}{3}}} . Here the weights, which necessarily sum to the value one, are ( 2 / 3 ) {\displaystyle (2/3)} and ( 1 / 3 ) {\displaystyle (1/3)} , the former being twice the latter. The arithmetic mean (sometimes called the "unweighted average" or "equally weighted average") can be interpreted as a special case of a weighted average in which all the weights are equal to each other (equal to 1 2 {\displaystyle {\frac {1}{2}}} in the above example, and equal to 1 n {\displaystyle {\frac {1}{n}}} in a situation with n {\displaystyle n} numbers being averaged). Continuous probability distributionsComparison of two log-normal distributions with equal median, but different skewness, resulting in different means and modesIf a numerical property, and any sample of data from it, could take on any value from a continuous range, instead of, for example, just integers, then the probability of a number falling into some range of possible values can be described by integrating a continuous probability distribution across this range, even when the naive probability for a sample number taking one certain value from infinitely many is zero. The analog of a weighted average in this context, in which there are an infinite number of possibilities for the precise value of the variable in each range, is called the mean of the probability distribution. A most widely encountered probability distribution is called the normal distribution; it has the property that all measures of its central tendency, including not just the mean but also the aforementioned median and the mode (the three Ms[6]), are equal to each other. This equality does not hold for other probability distributions, as illustrated for the log-normal distribution here. AnglesParticular care is needed when using cyclic data, such as phases or angles. Naively taking the arithmetic mean of 1° and 359° yields a result of 180°. This is incorrect for two reasons:
In general application, such an oversight will lead to the average value artificially moving towards the middle of the numerical range. A solution to this problem is to use the optimization formulation (viz., define the mean as the central point: the point about which one has the lowest dispersion), and redefine the difference as a modular distance (i.e., the distance on the circle: so the modular distance between 1° and 359° is 2°, not 358°). Proof without words of the inequality of arithmetic and geometric means:PR is a diameter of a circle centred on O; its radius AO is the arithmetic mean of a and b. Using the geometric mean theorem, triangle PGR's altitude GQ is the geometric mean. For any ratio a:b, AO ≥ GQ. Symbols and encodingThe arithmetic mean is often denoted by a bar, (a.k.a. vinculum or macron), for example as in x ¯ {\displaystyle {\bar {x}}} (read x {\displaystyle x} bar).[2] Some software (text processors, web browsers) may not display the x̄ symbol properly. For example, the x̄ symbol in HTML is actually a combination of two codes - the base letter x plus a code for the line above (̄ or ¯).[7] In some texts, such as pdfs, the x̄ symbol may be replaced by a cent (¢) symbol (Unicode ¢), when copied to text processor such as Microsoft Word. See alsoGeometric proof without words that max (a,b) > root mean square (RMS) or quadratic mean (QM) > arithmetic mean (AM) > geometric mean (GM) > harmonic mean (HM) > min (a,b) of two distinct positive numbers a and b [8]
References
Further reading
External links
|