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This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
It does this by representing data as points in a low-dimensional Euclidean space. The procedure thus appears to be the counterpart of principal component analysis for categorical data. [citation needed] MCA can be viewed as an extension of simple correspondence analysis (CA) in that it is applicable to a large set of categorical variables.
Categorical quantum mechanics is the study of quantum foundations and quantum information using paradigms from mathematics and computer science, notably monoidal category theory. The primitive objects of study are physical processes, and the different ways these can be composed. It was pioneered in 2004 by Samson Abramsky and Bob Coecke.
Applied category theory is an academic discipline in which methods from category theory are used to study other fields [1] [2] [3] including but not limited to ...
an epimorphism (or epic) if g 1 ∘ f = g 2 ∘ f implies g 1 = g 2 for all morphisms g 1, g 2 : b → x. a bimorphism if f is both epic and monic. an isomorphism if there exists a morphism g : b → a such that f ∘ g = 1 b and g ∘ f = 1 a. [c] an endomorphism if a = b. end(a) denotes the class of endomorphisms of a.
If F : C → D is an equivalence of categories, and G 1 and G 2 are two inverses of F, then G 1 and G 2 are naturally isomorphic. If F : C → D is an equivalence of categories, and if C is a preadditive category (or additive category , or abelian category ), then D may be turned into a preadditive category (or additive category, or abelian ...
The parameters specifying the probabilities of each possible outcome are constrained only by the fact that each must be in the range 0 to 1, and all must sum to 1. The categorical distribution is the generalization of the Bernoulli distribution for a categorical random variable, i.e. for a discrete variable with more than two possible outcomes ...
The Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, is a measure of the strength and direction of the linear relationship between two variables that is defined as the covariance of the variables divided by the product of their standard deviations. [4]