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Random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events [1] iid was first defined in statistics and finds application in many fields, such as data mining and signal. The term 'random variable' in its mathematical definition refers to neither randomness nor variability[1] but instead is a mathematical function in which the domain is the set of possible outcomes in a sample space (e.g.
In some contexts, a variable can be discrete in some ranges of the number line and continuous in others A chart showing a uniform distribution in probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or iid) if each random variable has the same probability distribution as the others and all are mutually independent In statistics, continuous and discrete variables are distinct statistical data types which are described with different probability distributions.
A variable is considered dependent if it depends on (or is hypothesized to depend on) an independent variable
Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Statistics the normal distribution, a very common probability density, is used extensively in inferential statistics Scatter plots and line charts are used in descriptive statistics to show the observed relationships between different variables, here using the iris flower data set. In statistics, latent variables (from latin
Present participle of lateo 'lie hidden'[citation needed]) are variables that can only be inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured [1] such latent variable models are used in many disciplines, including engineering, medicine, ecology, physics, machine learning. The joint probability distribution of random variables x and y is denoted as , while joint probability mass function or probability density function as and joint cumulative distribution function as Or indicates the probability of either event a or event b occurring (or in this case means one or the other or both).
For additional related terms, see glossary of mathematics and glossary of experimental design.
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