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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.
In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space .
Univariate analysis is the simplest form of analyzing data. Uni means "one", so the data has only one variable . [4] Univariate data requires to analyze each variable separately. Data is gathered for the purpose of answering a question, or more specifically, a research question.
Correspondence analysis (CA) is a multivariate statistical technique proposed [1] by Herman Otto Hartley (Hirschfeld) [2] and later developed by Jean-Paul Benzécri. [3] It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data. In a similar manner to principal component analysis, it ...
Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term "categorical data" to apply to data sets that, while containing some categorical variables, may also contain non-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]
When classification is performed by a computer, statistical methods are normally used to develop the algorithm.. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
Data analysis is the process of inspecting, cleansing, ... Data may be numerical or categorical (i.e., a text label for numbers). [13] Data collection.