- Weighted mean
The

**weighted mean**is similar to anarithmetic mean (the most common type ofaverage ), where instead of each of the data points contributing equally to the final average, some data points contribute more than others. The notion of weighted mean plays a role indescriptive statistics and also occurs in a more general form in several other areas of mathematics.If all the weights are equal, then the weighted mean is the same as the

arithmetic mean . While weighted means generally behave in a similar fashion to arithmetic means, they do have a few counter-intuitive properties, as captured for instance inSimpson's paradox .The term

**weighted average**usually refers to a weighted arithmetic mean, but weighted versions of other means can also be calculated, such as theweighted geometric mean and theweighted harmonic mean .**Example**Given two school classes, one with 20 students, and one with 30 students, the grades in each class on a test were:

:Morning class = 62, 67, 71, 74, 76, 77, 78, 79, 79, 80, 80, 81, 81, 82, 83, 84, 86, 89, 93, 98

:Afternoon class = 81, 82, 83, 84, 85, 86, 87, 87, 88, 88, 89, 89, 89, 90, 90, 90, 90, 91, 91, 91, 92, 92, 93, 93, 94, 95, 96, 97, 98, 99

The straight average for the morning class is 80 and the straight average of the afternoon class is 90. The straight average of 80 and 90 is 85, the mean of the two class means. However, this does not account for the difference in number of students in each class, and the value of 85 does not reflect the average student grade (independent of class). The average student grade can be obtained by either averaging all the numbers without regard to classes, or weighting the class means by the number of students in each class:

:$ar\{x\}\; =\; frac\{4300\}\{50\}\; =\; 86.$

Or, using a weighted mean of the class means:

:$ar\{x\}\; =\; frac\{(20)80\; +\; (30)90\}\{20\; +\; 30\}\; =\; 86.$

The weighted mean makes it possible to find the average student grade also in the case where only the class means and the number of students in each class are available.

**Mathematical definition**Formally, the weighted mean of a

non-empty set of data:$[x\_1,\; x\_2,\; dots\; ,\; x\_n]\; ,,$

with non-negative weights

:$[w\_1,\; w\_2,\; dots,\; w\_n]\; ,,$

is the quantity calculated by

:$ar\{x\}\; =\; frac\{\; sum\_\{i=1\}^n\; w\_i\; x\_i\}\{sum\_\{i=1\}^n\; w\_i\},$

which means:

:$ar\{x\}\; =\; frac\{w\_1\; x\_1\; +\; w\_2\; x\_2\; +\; cdots\; +\; w\_n\; x\_n\}\{w\_1\; +\; w\_2\; +\; cdots\; +\; w\_n\}.$

So data elements with a high weight contribute more to the weighted mean than do elements with a low weight. The weights must not be negative. They may be zero, but not all of them (because division by zero is not allowed). In the special case, often encountered in practice, where the weights are normalized (i.e. are nonnegative and sum up to 1), the denominator of the fraction simplifies to 1.

**Length-weighted mean**For weighting a response variable based upon its dependency on "x", a distance variable.

:$ar\{y\}\; =\; frac\{y\_2\; x\_2\; -\; y\_1\; x\_1\}\{x\_2\; -\; x\_1\}$

**Convex combination**Since only the "relative" weights are relevant, any weighted mean can be expressed using coefficients that sum to one. Such a linear combination is called a

convex combination .Using the previous example, we would get the following:

:$frac\{20\}\{20\; +\; 30\}\; =\; 0.4,$

:$frac\{30\}\{20\; +\; 30\}\; =\; 0.6,$

:$ar\{x\}\; =\; frac\{(0.4)80\%\; +\; (0.6)90\%\}\{0.4\; +\; 0.6\}\; =\; 86\%$

This simplifies to:

:$ar\{x\}\; =\; (0.4)80\%\; +\; (0.6)90\%\; =\; 86\%$

**Dealing with variance**For the weighted mean of a list of data for which each element $x\_i,!$ comes from a different

probability distribution with knownvariance $\{sigma\_i\}^2,$, one possible choice for the weights is given by::$w\_i\; =\; frac\{1\}\{sigma\_i^2\}.$

The weighted mean in this case is:

:$ar\{x\}\; =\; frac\{\; sum\_\{i=1\}^n\; x\_i/\{sigma\_i\}^2\}\{sum\_\{i=1\}^n\; 1/\{sigma\_i\}^2\},$

and the variance of the weighted mean is:

:$sigma\_\{ar\{x^2\; =\; frac\{\; 1\; \}\{sum\_\{i=1\}^n\; 1/\{sigma\_i\}^2\},$

which reduces to $sigma\_\{ar\{x^2\; =\; frac\{\; \{sigma\_0\}^2\; \}\{n\}$, when all $sigma\_i\; =\; sigma\_0.,$

The significance of this choice is that this weighted mean is the

maximum likelihood estimator of the mean of the probability distributions under the assumption that they are independent andnormally distributed with the same mean.**Correcting for over/under dispersion**Weighted means are typically used to find the weighted mean of experimental data, rather than theoretically generated data. In this case, there will be some error in the variance of each data point. Typically experimentally errors are underestimated, because the experimenter does not know all sources of error in calculating the variance of each data point. In this event, the variance in the weighted mean must be corrected to account for the fact that $chi^2$ is too large. The correction that must be made is

:$sigma\_\{ar\{x^2\; ightarrow\; sigma\_\{ar\{x^2\; chi^2\_\; u$

where $chi^2\_\; u,$ is $chi^2,$ divided by the number of degrees of freedom, in this case $n-1,$. This gives the variance in the weighted mean as:

:$sigma\_\{ar\{x^2\; =\; frac\{\; 1\; \}\{sum\_\{i=1\}^n\; 1/\{sigma\_i\}^2\}\; imes\; frac\{1\}\{(n-1)\}\; sum\_\{i=1\}^n\; frac\{\; (x\_i\; -\; ar\{x\}\; )^2\}\{\; sigma\_i^2\; \}\; ,$

**Weighted sample variance**Typically when you calculate a mean it is important to know the

variance andstandard deviation of that mean. When a weighted mean $mu^*$ is used, the variance of the weighted sample is different from the variance of the unweighted sample. The "biased" weightedsample variance is defined similarly to the normal "biased" sample variance::$sigma^2\; =\; frac\{\; sum\_\{i=1\}^N\{left(x\_i\; -\; mu\; ight)^2\}\; \}\{\; N\; \};;;sigma\_\; ext\{weighted\}^2\; =\; frac\{\; sum\_\{i=1\}^N$w_i}left(x_i - mu^* ight)^2} }{ sum_{i=1}^N{w_i} }.

For small sample of populations, it is customary to use an

unbiased estimator for the population variance. In normal unweighted samples, the "N" in the denominator (corresponding to the sample size) is changed to "N" − 1. While this is simple in unweighted samples, it becomes tedious for weighted samples. Thus, the unbiased estimator of weighted population variance is given by [*http://pygsl.sourceforge.net/reference/pygsl/node36.html*] ::$s^2\; =\; frac\{\; sum\_\{i=1\}^N\{w\_i\}\; \}\{\; left(sum\_\{i=1\}^N\{w\_i\}\; ight)^2\; -\; sum\_\{i=1\}^N$w_i}^2} } sum_{i=1}^Nw_i}left(x_i - mu^* ight)^2}.

Which can also be written in terms of running sums for programming as:

:$s^2\; =\; frac\{\; sum\_\{i=1\}^N\{w\_i\; \{x\_i\}^2\}\; sum\_\{i=1\}^N\{w\_i\}\; -\; left(sum\_\{i=1\}^N\{w\_i\; x\_i\}\; ight)^2\}\{\; left(sum\_\{i=1\}^N\{w\_i\}\; ight)^2\; -\; sum\_\{i=1\}^N$w_i}^2.

The standard deviation is simply the square root of the variance above.

**Accounting for correlations**In the general case, suppose that $mathbf\{X\}=\; [x\_1,dots,x\_n]$, $mathbf\{C\}$ is the

covariance matrix relating the quantities $x\_i$, $ar\{x\}$ is the common mean to be estimated, and $mathbf\{W\}$ is thedesign matrix [1, ..., 1] (of length "n"). TheGauss–Markov theorem says that the estimate of the mean having minimum variance is given by::$sigma^2\_ar\{x\}=(mathbf\{W\}^T\; mathbf\{C\}^\{-1\}\; mathbf\{W\})^\{-1\},$

and

:$ar\{x\}\; =\; sigma^2\_ar\{x\}\; (mathbf\{W\}^T\; mathbf\{C\}^\{-1\}\; mathbf\{X\}).$

**ee also***

Average

*Summary statistics

*Central tendency

*Weighted least squares

*Weighted average cost of capital **References*** Bevington, Philip. "Data Reduction and Error Analysis for the Physical Sciences."

**External links*** [

*http://www.iop.org/EJ/abstract/0026-1394/43/3/002 The uncertainty associated with the weighted mean of measurement data*]

*Wikimedia Foundation.
2010.*

### Look at other dictionaries:

**weighted mean**— svertinis aritmetinis vidurkis statusas T sritis Standartizacija ir metrologija apibrėžtis Apibrėžtį žr. priede. priedas( ai) Grafinis formatas atitikmenys: angl. arithmetic weighted mean; weighted arithmetic average; weighted average; weighted… … Penkiakalbis aiškinamasis metrologijos terminų žodynas**weighted mean**— svertinis aritmetinis vidurkis statusas T sritis fizika atitikmenys: angl. arithmetic weighted mean; weighted arithmetic average; weighted mean vok. Gewichteter arithmetischer Mittelwert, m; gewogener Mittelwert, m; gewogenes Mittelwert, m rus.… … Fizikos terminų žodynas**weighted mean**— svertinis vidurkis statusas T sritis fizika atitikmenys: angl. weighted arithmetic average; weighted average; weighted mean vok. gewogener Mittelwert, m; gewogenes Mittel, n rus. взвешенное среднее, n; средневзвешенное значение, n pranc. moyenne… … Fizikos terminų žodynas**weighted mean**— weighted average … Accounting dictionary**weighted mean**— noun see weighted average * * * Statistics. a mean that is computed with extra weight given to one or more elements of the sample. Also called weighted average. [1835 45] … Useful english dictionary**weighted mean**— Statistics. a mean that is computed with extra weight given to one or more elements of the sample. Also called weighted average. [1835 45] * * * … Universalium**weighted mean**— noun An average calculated after biasing values according to agreed weightings. Syn: weighted average … Wiktionary**weighted mean**— An alternative term for *weighted average … Auditor's dictionary**arithmetic weighted mean**— svertinis aritmetinis vidurkis statusas T sritis Standartizacija ir metrologija apibrėžtis Apibrėžtį žr. priede. priedas( ai) Grafinis formatas atitikmenys: angl. arithmetic weighted mean; weighted arithmetic average; weighted average; weighted… … Penkiakalbis aiškinamasis metrologijos terminų žodynas**arithmetic weighted mean**— svertinis aritmetinis vidurkis statusas T sritis fizika atitikmenys: angl. arithmetic weighted mean; weighted arithmetic average; weighted mean vok. Gewichteter arithmetischer Mittelwert, m; gewogener Mittelwert, m; gewogenes Mittelwert, m rus.… … Fizikos terminų žodynas