Random variable | Wikipedia audio article
Channel: wikipedia tts
Date: 10/05/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Random_variable


00:02:30 1 Definition
00:04:17 1.1 Standard case
00:05:48 1.2 Extensions
00:09:54 2 Distribution functions
00:13:19 3 Examples
00:13:28 3.1 Discrete random variable
00:15:34 3.1.1 Coin toss
00:16:30 3.1.2 Dice roll
00:18:13 3.2 Continuous random variable
00:22:42 3.3 Mixed type
00:23:19 4 Measure-theoretic definition
00:23:58 4.1 Real-valued random variables
00:28:19 5 Moments
00:30:06 6 Functions of random variables
00:33:07 6.1 Example 1
00:39:36 6.2 Example 2
00:41:23 6.3 Example 3
00:45:15 6.4 Example 4
00:49:17 7 Equivalence of random variables
00:54:30 7.1 Equality in distribution
00:54:56 7.2 Almost sure equality
00:56:09 7.3 Equality
00:57:31 8 Convergence
00:58:12 9 Notes
00:58:50 10 See also



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SUMMARY
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In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is described informally as a variable whose values depend on outcomes of a random phenomenon. The formal mathematical treatment of random variables is a topic in probability theory. In that context, a random variable is understood as a measurable function defined on a sample space whose outcomes are typically real numbers.

A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, because of imprecise measurements or quantum uncertainty).  They may also conceptually represent either the results of an "objectively" random process (such as rolling a die) or the "subjective" randomness that results from incomplete knowledge of a quantity.  The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself but is instead related to philosophical arguments over the interpretation of probability.  The mathematics works the same regardless of the particular interpretation in use.
As a function, a random variable is required to be measurable, which allows for probabilities to be assigned to sets of its potential values. It is common that the outcomes depend on some physical variables that are not predictable. For example, when tossing a fair coin, the final outcome of heads or tails depends on the uncertain physical conditions. Which outcome will be observed is not certain. The coin could get caught in a crack in the floor, but such a possibility is excluded from consideration. 
The domain of a random variable is a sample space, which is interpreted as the set of possible outcomes of a random phenomenon. For example, in the case of a coin toss, only two possible outcomes are considered, namely heads or tails.
A random variable has a probability distribution, which specifies the probability of its values. Random variables can be discrete, that is, taking any of a specified finite or countable list of values, endowed with a probability mass function characteristic of the random variable's probability distribution; or continuous, taking any numerical value in an interval or collection of intervals, via a probability density function that is characteristic of the random variable's probability distribution; or a mixture of both types. 
Two random variables with the same probability distribution can still differ in terms of their associations with, or independence from, other random variables. The realizations of a random variable, that is, the results of randomly choosing values according to the variable's probability distribution function, are called random variates.