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The category of matrices is equivalent to the category of finite-dimensional real vector spaces and linear maps. This is witnessed by the functor mapping the number n {\displaystyle n} to the vector space R n {\displaystyle \mathbb {R} ^{n}} , and an n × m {\displaystyle n\times m} matrix to the corresponding linear map R m → R n ...
A matrix (plural matrices, or less commonly matrixes) is a rectangular array of numbers called entries. Matrices have a long history of both study and application, leading to diverse ways of classifying matrices. A first group is matrices satisfying concrete conditions of the entries, including constant matrices.
Statistics also makes use of matrices in many different forms. [90] ... Category of matrices — The algebraic structure formed by matrices and their multiplication;
Pages in category "Matrices" The following 200 pages are in this category, out of approximately 235 total. ... Statistics; Cookie statement; Mobile view ...
Matrix theory is a branch of mathematics that focuses on the study of matrices. It was initially a sub-branch of linear algebra , but soon grew to include subjects related to graph theory , algebra , combinatorics and statistics .
The design matrix has dimension n-by-p, where n is the number of samples observed, and p is the number of variables measured in all samples. [4] [5]In this representation different rows typically represent different repetitions of an experiment, while columns represent different types of data (say, the results from particular probes).
MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]
Random matrices. Random matrices can be laid out linearly and treated as random vectors; however, this may not be an efficient way of representing the correlations between different elements. Some probability distributions are specifically designed for random matrices, e.g. the matrix normal distribution and Wishart distribution. Random sequences.