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The formal mathematical treatment of random variables is dealt with in the subject of [[probability theory]]. In that context, random variables are defined in terms of [[Measurable function|functions]] defined on a [[probability space]].
==Introduction==
Real-valued random variables (those whose range is the [[real numbers]]) are used in the sciences to make predictions based on data obtained from [[experiment|scientific experiments]].{{citation needed|date=June 2012}} In addition to scientific applications, random variables were developed for the analysis of [[Game of chance|games of chance]] and [[stochastic]] events. In such instances, the function that maps the outcome to a real number is often the [[identity function]] or similarly trivial function, and not explicitly described. In many cases, however, it is useful to consider random variables that are functions of other random variables, and then the mapping function included in the definition of a random variable becomes important. As an example, the square of a random variable distributed according to a [[standard normal]] distribution is itself a random variable, with a [[chi-squared distribution]]. One way to think of this is to imagine generating a large number of samples from a standard normal distribution, squaring each one, and plotting a histogram of the values observed. With enough samples, the graph of the histogram will approximate the [[density function]] of a chi-squared distribution with one [[degree of freedom (statistics)|degree of freedom]].
Another example is the [[sample mean]], which is the average of a number of samples. When these samples are independent observations of the same random event they can be called [[independent identically distributed]] random variables. Since each sample is a random variable, the sample mean is a function of random variables and hence a random variable itself, whose distribution can be computed and properties determined.
One of the reasons that real-valued random variables are so commonly considered is that the [[expected value]] (a type of average) and [[variance]] (a measure of the "spread", or extent to which the values are dispersed) of the variable can be computed.{{citation needed|date=June 2012}}
There are several types of random variables, the most common two are the discrete and the continuous.<ref>{{cite book
|last = Rice
|first = John
|title = Mathematical Statistics and Data Analysis
|publisher = Duxbury Press
|year = 1999
|isbn = 0-534-20934-3}}</ref> A [[Discrete probability distribution|discrete]] random variable maps outcomes to values of a countable set (e.g., the [[integer]]s), with each value in the [[Range (mathematics)|range]] having probability greater than or equal to zero. A [[Continuous probability distribution|continuous]] random variable maps outcomes to values of an uncountable set (e.g., the [[real number]]s). For a continuous random variable, the probability of any specific value is zero, whereas the probability of some infinite set of values (such as an interval of non-zero length) may be positive. A random variable can be "mixed", with part of its probability spread out over an interval like a typical continuous variable, and part of it concentrated on particular values like a discrete variable. These classifications are equivalent to the categorization of [[probability distribution]]s.
The expected value of [[random vector]]s, [[random matrix|random matrices]], and similar aggregates of fixed structure is defined as the aggregation of the expected value computed over each individual element. The concept of "variance of a random vector" is normally expressed through a [[covariance matrix]]. No generally-agreed-upon definition of expected value or variance exists for cases other than just discussed.
==Definition==
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