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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.
Given a set of specific cases, find attributes of those cases. What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}? - What is the mileage per gallon of the Ford Mondeo? - How long is the movie Gone with the Wind? 2 Filter: Given some concrete conditions on attribute values, find data cases satisfying those conditions.
Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point ...
A categorical variable that can take on exactly two values is termed a binary variable or a dichotomous variable; an important special case is the Bernoulli variable. Categorical variables with more than two possible values are called polytomous variables; categorical variables are often assumed to be polytomous unless otherwise specified.
The Burt table is the symmetric matrix of all two-way cross-tabulations between the categorical variables, and has an analogy to the covariance matrix of continuous variables. Analyzing the Burt table is a more natural generalization of simple correspondence analysis , and individuals or the means of groups of individuals can be added as ...
Algorithms of this nature use statistical inference to find the best class for a given instance. Unlike other algorithms, which simply output a "best" class, probabilistic algorithms output a probability of the instance being a member of each of the possible classes. The best class is normally then selected as the one with the highest probability.
function draw_categorical(n) // where n is the number of samples to draw from the categorical distribution r = 1 s = 0 for i from 1 to k // where k is the number of categories v = draw from a binomial(n, p[i] / r) distribution // where p[i] is the probability of category i for j from 1 to v z[s++] = i // where z is an array in which the results ...
Summary statistics for categorical data (1 C, 6 P) V. Categorical variable interactions (2 C, 7 P) Pages in category "Categorical data"