Part of a series on statistics 
Probability theory 

In probability, and statistics, a multivariate random variable or random vector is a list or vector of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value. The individual variables in a random vector are grouped together because they are all part of a single mathematical system — often they represent different properties of an individual statistical unit. For example, while a given person has a specific age, height and weight, the representation of these features of an unspecified person from within a group would be a random vector. Normally each element of a random vector is a real number.
Random vectors are often used as the underlying implementation of various types of aggregate random variables, e.g. a random matrix, random tree, random sequence, stochastic process, etc.
More formally, a multivariate random variable is a column vector (or its transpose, which is a row vector) whose components are scalarvalued random variables on the same probability space as each other, , where is the sample space, is the sigmaalgebra (the collection of all events), and is the probability measure (a function returning each event's probability).
Main article: Multivariate probability distribution 
Every random vector gives rise to a probability measure on with the Borel algebra as the underlying sigmaalgebra. This measure is also known as the joint probability distribution, the joint distribution, or the multivariate distribution of the random vector.
The distributions of each of the component random variables are called marginal distributions. The conditional probability distribution of given is the probability distribution of when is known to be a particular value.
The cumulative distribution function of a random vector is defined as^{[1]}^{: p.15 }

(Eq.1)

where .
Random vectors can be subjected to the same kinds of algebraic operations as can nonrandom vectors: addition, subtraction, multiplication by a scalar, and the taking of inner products.
Similarly, a new random vector can be defined by applying an affine transformation to a random vector :
If is an invertible matrix and has a probability density function , then the probability density of is
More generally we can study invertible mappings of random vectors.^{[2]}^{: p.290–291 }
Let be a onetoone mapping from an open subset of onto a subset of , let have continuous partial derivatives in and let the Jacobian determinant of be zero at no point of . Assume that the real random vector has a probability density function and satisfies . Then the random vector is of probability density
where denotes the indicator function and set denotes support of .
The expected value or mean of a random vector is a fixed vector whose elements are the expected values of the respective random variables.^{[3]}^{: p.333 }

(Eq.2)

The covariance matrix (also called second central moment or variancecovariance matrix) of an random vector is an matrix whose (i,j)^{th} element is the covariance between the i^{ th} and the j^{ th} random variables. The covariance matrix is the expected value, element by element, of the matrix computed as , where the superscript T refers to the transpose of the indicated vector:^{[2]}^{: p. 464 }^{[3]}^{: p.335 }

(Eq.3)

By extension, the crosscovariance matrix between two random vectors and ( having elements and having elements) is the matrix^{[3]}^{: p.336 }

(Eq.4)

where again the matrix expectation is taken elementbyelement in the matrix. Here the (i,j)^{th} element is the covariance between the i^{ th} element of and the j^{ th} element of .
The covariance matrix is a symmetric matrix, i.e.^{[2]}^{: p. 466 }
The covariance matrix is a positive semidefinite matrix, i.e.^{[2]}^{: p. 465 }
The crosscovariance matrix is simply the transpose of the matrix , i.e.
Two random vectors and are called uncorrelated if
They are uncorrelated if and only if their crosscovariance matrix is zero.^{[3]}^{: p.337 }
The correlation matrix (also called second moment) of an random vector is an matrix whose (i,j)^{th} element is the correlation between the i^{ th} and the j^{ th} random variables. The correlation matrix is the expected value, element by element, of the matrix computed as , where the superscript T refers to the transpose of the indicated vector:^{[4]}^{: p.190 }^{[3]}^{: p.334 }

(Eq.5)

By extension, the crosscorrelation matrix between two random vectors and ( having elements and having elements) is the matrix

(Eq.6)

The correlation matrix is related to the covariance matrix by
Similarly for the crosscorrelation matrix and the crosscovariance matrix:
Two random vectors of the same size and are called orthogonal if
Main article: Independence (probability theory) 
Two random vectors and are called independent if for all and
where and denote the cumulative distribution functions of and and denotes their joint cumulative distribution function. Independence of and is often denoted by . Written componentwise, and are called independent if for all
The characteristic function of a random vector with components is a function that maps every vector to a complex number. It is defined by^{[2]}^{: p. 468 }
One can take the expectation of a quadratic form in the random vector as follows:^{[5]}^{: p.170–171 }
where is the covariance matrix of and refers to the trace of a matrix — that is, to the sum of the elements on its main diagonal (from upper left to lower right). Since the quadratic form is a scalar, so is its expectation.
Proof: Let be an random vector with and and let be an nonstochastic matrix.
Then based on the formula for the covariance, if we denote and , we see that:
Hence
which leaves us to show that
This is true based on the fact that one can cyclically permute matrices when taking a trace without changing the end result (e.g.: ).
We see that
And since
is a scalar, then
trivially. Using the permutation we get:
and by plugging this into the original formula we get:
One can take the expectation of the product of two different quadratic forms in a zeromean Gaussian random vector as follows:^{[5]}^{: pp. 162–176 }
where again is the covariance matrix of . Again, since both quadratic forms are scalars and hence their product is a scalar, the expectation of their product is also a scalar.
In portfolio theory in finance, an objective often is to choose a portfolio of risky assets such that the distribution of the random portfolio return has desirable properties. For example, one might want to choose the portfolio return having the lowest variance for a given expected value. Here the random vector is the vector of random returns on the individual assets, and the portfolio return p (a random scalar) is the inner product of the vector of random returns with a vector w of portfolio weights — the fractions of the portfolio placed in the respective assets. Since p = w^{T}, the expected value of the portfolio return is w^{T}E() and the variance of the portfolio return can be shown to be w^{T}Cw, where C is the covariance matrix of .
In linear regression theory, we have data on n observations on a dependent variable y and n observations on each of k independent variables x_{j}. The observations on the dependent variable are stacked into a column vector y; the observations on each independent variable are also stacked into column vectors, and these latter column vectors are combined into a design matrix X (not denoting a random vector in this context) of observations on the independent variables. Then the following regression equation is postulated as a description of the process that generated the data:
where β is a postulated fixed but unknown vector of k response coefficients, and e is an unknown random vector reflecting random influences on the dependent variable. By some chosen technique such as ordinary least squares, a vector is chosen as an estimate of β, and the estimate of the vector e, denoted , is computed as
Then the statistician must analyze the properties of and , which are viewed as random vectors since a randomly different selection of n cases to observe would have resulted in different values for them.
The evolution of a k×1 random vector through time can be modelled as a vector autoregression (VAR) as follows:
where the iperiodsback vector observation is called the ith lag of , c is a k × 1 vector of constants (intercepts), A_{i} is a timeinvariant k × k matrix and is a k × 1 random vector of error terms.