# Per capita income

Per capita income, more simply known as income per person, is the mean income within an economic aggregate such as a country or city. It is calculated by taking a measure of all sources of income in the aggregate (such as GDP or Gross national income) and dividing it by the total population. Per capita income as a measure of prosperity Per capita income is often used as average income, a measure of the wealth of the population of a nation, particularly in comparison to other nations. It is usually expressed in terms of a commonly used international currency such as the Euro or United States dollar, and is useful because it is widely known, easily calculated from readily-available GDP and population estimates, and produces a useful statistic for comparison of wealth between sovereign territories. Critics claim that per capita income has several weaknesses as an accurate measurement of prosperity: Comparisons of per capita income over time need to take into account changes in prices. Without using measures of income adjusted for inflation, they will tend to overstate the effects of economic growth. International comparisons can be distorted by differences in the costs of living between countries that aren't reflected in exchange rates. Where the objective of the comparison is to look at differences in living standards between countries, using a measure of per capita income adjusted for differences in purchasing power parity more accurately reflects the differences in what people are actually able to buy with their money. From Wikipedia, the free encyclopedia This article is about the statistical concept. For other uses, see Mean (disambiguation). In statistics, mean has three related meanings[1] : the arithmetic mean of a sample (distinguished from the geometric mean or harmonic mean). the expected value of a random variable. the mean of a probability distribution. Th re are other statistical measures of central tendency that should not be confused with means - including the 'median' and 'mode'. Statistical analyses also commonly use measures of dispersion, such as the range, interquartile range, or standard deviation. Note that not every probability distribution has a defined mean; see the Cauchy distribution for an example. For a data set, the arithmetic mean is equal to the sum of the values divided by the number of values. The arithmetic mean of a set of numbers x1, x2, ..., xn is typically denoted by , pronounced "x bar". If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is termed the "sample mean" () to distinguish it from the "population mean" ( or x).[2] For a finite population, the population mean of a property is equal to the arithmetic mean of the given property while considering every member of the population. For example, the population mean height is equal to the sum of the heights of every individual divided by the total number of individuals. The sample mean may differ from the population mean, especially for small samples. The law of large numbers dictates that the larger the size of the sample, the more likely it is that the sample mean will be close to the population mean.[3] For a probability distribution, the mean is equal to the sum or integral over every possible value weighted by the probability of that value. In the case of a discrete probability distribution, the mean of a discrete random variable x is computed by taking the product of each possible value of x and its probability P(x), and then adding all these products together, giving .[4] As well as statistics, means are often used in geometry and analysis; a wide range of means have been developed for these purposes, which are not much used in statistics. Examples of means are listed below.

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