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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.
Pages for logged out editors learn more. Contributions; Talk; Categorical data analysis
Summary statistics for categorical data (1 C, 6 P) V. Categorical variable interactions (2 C, 7 P) Pages in category "Categorical data"
Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations , or from observations of quantitative data ...
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.
Scaling of data: One of the properties of the tests is the scale of the data, which can be interval-based, ordinal or nominal. [3] Nominal scale is also known as categorical. [6] Interval scale is also known as numerical. [6] When categorical data has only two possibilities, it is called binary or dichotomous. [1]
Data analysis is the process of inspecting, cleansing, ... Data may be numerical or categorical (i.e., a text label for numbers). [13] Data collection.
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.