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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.
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 ...
Agresti has written several books on categorical data analysis, including An Introduction to Categorical Data Analysis and Categorical Data Analysis. Other textbooks include the following: Statistics: The Art and Science of Learning from Data; Statistical Methods for the Social Sciences; Analysis of Ordinal Categorical Data, Second Edition
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] Assumptions, parametric and non-parametric: There are two groups of statistical tests, parametric and non-parametric. The choice between these two groups needs ...
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. These properties may variously be categorical (e.g.
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.
For data that is numerical, all three measures are possible. If the distribution of data is symmetrical, then the measures of variability are usually the variance and standard deviation. However, if the data are skewed, then the measure of variability that would be appropriate for that data set is the range. [3]
In statistics, data can have any of various types.Statistical data types include categorical (e.g. country), directional (angles or directions, e.g. wind measurements), count (a whole number of events), or real intervals (e.g. measures of temperature).