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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.
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]
Given a set of data cases, rank them according to some ordinal metric. What is the sorted order of a set S of data cases according to their value of attribute A? - Order the cars by weight. - Rank the cereals by calories. 6 Determine Range: Given a set of data cases and an attribute of interest, find the span of values within the set.
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.
A variable used to associate each data point in a set of observations, or in a particular instance, to a certain qualitative category is a categorical variable. Categorical variables have two types of scales, ordinal and nominal. [1] The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality.
Categorical logic, a branch of category theory within mathematics with notable connections to theoretical computer science; Categorical syllogism, a kind of logical argument; Categorical proposition, a part of deductive reasoning; Categorization; Categorical perception; Category theory in mathematics Categorical set theory; Categorical probability
The null distribution of the Pearson statistic with j rows and k columns is approximated by the chi-squared distribution with (k − 1)(j − 1) degrees of freedom. [ 12 ] This approximation arises as the true distribution, under the null hypothesis, if the expected value is given by a multinomial distribution .
Summary statistics for categorical data (1 C, 6 P) V. Categorical variable interactions (2 C, 7 P) Pages in category "Categorical data"