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Variable capable of taking on a limited number of possible values
In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of
Categorical_variable
Discrete probability distribution
The categorical distribution is the generalization of the Bernoulli distribution for a categorical random variable, i.e. for a discrete variable with
Categorical_distribution
Numeric stand-ins in regression analysis
of some categorical effect that may be expected to shift the outcome. In machine learning this is known as one-hot encoding. Dummy variables are commonly
Dummy_variable_(statistics)
System for displaying information by using different colors
to be categorical (representing unordered/qualitative categories) though may also be sequential (representing an ordered/quantitative variable). The earliest
Color_code
Statistics concept
modifier). The effect of a moderating variable is characterized statistically as an interaction; that is, a categorical (e.g., sex, ethnicity, class) or continuous
Moderation_(statistics)
Distributions in probability theory
categorical variables dependent on multiple priors sharing a hyperprior; we have categorical variables with dependent children (the latent variable topic
Dirichlet-multinomial distribution
Dirichlet-multinomial_distribution
General linear model that blends ANOVA and regression
dependent variable (DV) are equal across levels of one or more categorical independent variables (IV) and across one or more continuous variables. For example
Analysis_of_covariance
Data whose unit can take on only two possible states
simply ignored. Modeling continuous data (or categorical data of more than 2 categories) as a binary variable for analysis purposes is called dichotomization
Binary_data
Topics referred to by the same term
variable, a variable in statistics whose value depends on random events Categorical variable, taking one of a finite number of values in a statistical problem
Variable
Visual representation of data
Categorical: Represent groups of objects with a particular characteristic. Categorical variables can either be nominal or ordinal. Nominal variables for
Data and information visualization
Data_and_information_visualization
Technique used in statistics
in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building
Log-linear_analysis
Statistical model for a binary dependent variable
for binary regression since about 1970. Binary variables can be generalized to categorical variables when there are more than two possible values (e
Logistic_regression
Bit-vector representation where only one bit can be set at a time
sometimes called one-cold. In statistics, dummy variables represent a similar technique for representing categorical data. One-hot encoding is often used for
One-hot
Monte Carlo algorithm
dynamically depending on some other variable (e.g. a categorical variable indexed by another latent categorical variable, as in a topic model), the same expected
Gibbs_sampling
Type of exploratory graph used in statistics
represent the levels of a categorical variable. A generalised biplot displays information on both continuous and categorical variables. The biplot was introduced
Biplot
Causal or moderating relationship between statistical variables
baseline, or untreated value of y. Sometimes the interacting variables are categorical variables rather than real numbers and the study might then be dealt
Interaction_(statistics)
Statistical model relating manifest and latent variables
A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent
Latent_variable_model
analysis of categorical data, also known as data on the nominal scale and as categorical variables. Bowker's test of symmetry Categorical distribution
List of analyses of categorical data
List_of_analyses_of_categorical_data
Measure for evaluating probabilistic forecasts
rules. The ones shown below are simply popular examples. For a categorical response variable with m {\displaystyle m} mutually exclusive events, Y ∈ Ω =
Scoring_rule
Types of numerical variables in mathematics
a quantitative variable may be continuous or discrete. If it can take on two real values and all the values between them, the variable is continuous in
Continuous or discrete variable
Continuous_or_discrete_variable
Method of representing variables in Bayesian inference
column size. Categorical variables are indicated by placing their size (without a bracket) in the middle of the node. Categorical variables that act as
Plate_notation
Taxonomy of statistical data elements
science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned
Statistical_data_type
Concept in statistics
category is a categorical variable. Categorical variables have two types of scales, ordinal and nominal. The first type of categorical scale is dependent
Nominal_category
Data analysis technique
cross-tabulations between the categorical variables, and has an analogy to the covariance matrix of continuous variables. Analyzing the Burt table is a
Multiple correspondence analysis
Multiple_correspondence_analysis
Method used in statistics, pattern recognition, and other fields
continuous dependent variable, whereas discriminant analysis has continuous independent variables and a categorical dependent variable (i.e. the class label)
Linear_discriminant_analysis
Statistic measuring inter-rater agreement for categorical items
statistic used to measure inter-rater reliability for qualitative or categorical data. It is generally thought to be a more robust measure than simple
Cohen's_kappa
Statistical measure of the magnitude of a phenomenon
size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, the
Effect_size
Properties not expressed numerically
A variable which codes for the presence or absence of such a property is called a binary categorical variable, or equivalently a dummy variable. Some
Qualitative_property
Variable representing a random phenomenon
A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which
Random_variable
Plot using the dispersal of scattered dots to show the relationship between variables
of categorical and quantitative variables. A mosaic plot, fluctuation diagram, or faceted bar chart may be used to display two categorical variables. Other
Scatter_plot
Choice of colors used in design
represent categorical variables, where the possible values of the variable are discrete and unordered. An example of a categorical variable is U.S. states
Color_scheme
Topics referred to by the same term
ratio of two like quantities Level, the different values that a categorical variable can have Level, the different values that a factor can have in factor
Level
Statistical measure
reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items. This contrasts with other kappas such as
Fleiss's_kappa
Statistical hypothesis test
simpler terms, this test is primarily used to examine whether two categorical variables (two dimensions of the contingency table) are independent in influencing
Chi-squared_test
Structure that categorizes facts and measures in a data warehouse
grouping by product. A dimensional data element is similar to a categorical variable in statistics. Typically dimensions in a data warehouse are organized
Dimension_(data_warehouse)
Numerical measure of a statistical relationship between variables
variables. The two continuous variables followed a bivariate normal distribution. When both variables are dichotomous instead of ordered-categorical,
Correlation_coefficient
Data analysis technique
of values of its independent and dependent variables. For instance, if there were four categorical variables of interest, {A,B,C,D}, and A and B were dichotomous
Qualitative comparative analysis
Qualitative_comparative_analysis
test the distributional differences between cases and controls for categorical variable set, which can be a single SNP, SNP-SNP, or SNP-environment pairs
W-test
Concept in statistical analysis
the dependent variable—the one whose value is determined to some extent by the other, independent variable— is a categorical variable, such as the preferred
Bivariate_analysis
Statistical data type
Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are
Ordinal_data
Data visualization
displays of two or more variables. The mosaic plot makes it possible to visualise relationships between different categorical variables. For example, independence
Mosaic_plot
Statistical model
an independent variable and a dependent variable, through the inclusion of a third hypothetical variable known as a mediator variable (also referred to
Mediation_(statistics)
Topics referred to by the same term
word in combinatorics or of a word in group theory. An independent categorical variable. In experimental design, the factor is a category of treatments controlled
Factor
Study of collection and analysis of data
science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned
Statistics
Graphical technique for data sets
trajectories. In the case of categorical variables, category level points may be used to represent the levels of a categorical variable. A generalised biplot
Plot_(graphics)
Mathematical function for the probability a given outcome occurs in an experiment
number of outcomes in a sample (i.e. sample size). Categorical distribution: for discrete random variables with a finite set of values. Absolutely continuous
Probability_distribution
Type of bar chart using dots
to depict the quantitative values (e.g. counts) associated with categorical variables. The dot plot as a representation of a distribution consists of
Dot_plot_(statistics)
Type of chart
A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that
Bar_chart
Set of statistical processes for estimating the relationships among variables
variables and some independent variables. For categorical variables with more than two values there is the multinomial logit. For ordinal variables with
Regression_analysis
of a categorical explanatory variable within a statistical model. In standard statistical models the effects of a categorical explanatory variable are
Quasi-variance
Mathematical methods used in Bayesian inference and machine learning
statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships
Variational_Bayesian_methods
Probability theory and statistics concept
parameter. When both X {\displaystyle X} and Y {\displaystyle Y} are categorical variables, a conditional probability table is typically used to represent
Conditional probability distribution
Conditional_probability_distribution
Perception of distinct categories in a variable along a continuum
Categorical perception is a phenomenon of perception of distinct categories when there is gradual change in a variable along a continuum. It was originally
Categorical_perception
continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable. They
Notation in probability and statistics
Notation_in_probability_and_statistics
Machine learning method
Logic Learning Machine for classification, when the output is a categorical variable, which can assume values in a finite set Logic Learning Machine for
Logic_learning_machine
Symbol representing a mathematical object
that the variable represents or denotes the object, and that any valid candidate for the object is the value of the variable. The values a variable can take
Variable_(mathematics)
Measure of inter-rater agreement
at the Wayback Machine, R package: Visualizing Categorical Data Friendly, M. "Working with categorical data with R and the vcd and vcdExtra packages"
Bangdiwala's_B
Sexual preference for pubescent children
supported victim age preferences being a continuous rather than categorical variable. In separate letters to the editor, forensic psychologist Gregory
Hebephilia
Machine learning algorithm
both numerical and categorical data. Other techniques are usually specialized in analyzing datasets that have only one type of variable. (For example, relation
Decision_tree_learning
Type of experimental design
model parameters to be estimated. Sort by a-1 columns assigned to categorical variable A and following columns, where A = 1 + int(a·i /(max(i) + 0.00001))
Plackett–Burman_design
Probability distribution
out the Dirichlet random variable. This causes the various categorical variables drawn from the same Dirichlet random variable to become correlated, and
Dirichlet_distribution
Statistical concept
latent variables specifying the identity of the mixture component of each observation, each distributed according to a K-dimensional categorical distribution
Mixture_model
multivariate visualization that displays numerical and categorical variables along parallel axes. Each variable occupies a vertical axis whose sections represent
Hammock_plot
Mathematical logic theory with exactly one countably infinite model up to isomorphism
{\displaystyle \aleph _{0}} = ω of κ-categoricity, and omega-categorical theories are also referred to as ω-categorical. The notion is most important for
Omega-categorical_theory
Probability distribution modeling a coin toss which need not be fair
\mathrm {Bernoulli} (p).} The categorical distribution is the generalization of the Bernoulli distribution for variables with any constant number of discrete
Bernoulli_distribution
Personality trait of highly sensitive persons
patterns in adults were thought to be distributed as a dichotomous categorical variable with a break point between 10% and 35%, with Aron choosing a cut-off
Sensory processing sensitivity
Sensory_processing_sensitivity
Distinction between nominal, ordinal, interval and ratio variables
1007/bf00485356. S2CID 46970420. "What is the difference between categorical, ordinal and interval variables?". Institute for Digital Research and Education. University
Level_of_measurement
Table that displays the frequency of variables
The relation between ordinal variables, or between ordinal and categorical variables, may also be represented in contingency tables, although such a
Contingency_table
Type of statistical model
effects in a model are allowed to vary, and when testing a dummy-coded categorical variable as a single effect. However, the test can only be used when models
Multilevel_model
Statistics of spatial association
the degree of association, in particular the autocorrelation, of categorical variables distributed over a spatial map. They were originally introduced
Join_count_statistic
Series of questions for gathering information
The nominal scale, also called the categorical variable scale, is defined as a scale used for labeling variables into distinct classifications and does
Questionnaire
of a multinomial distribution equal specified values; it is used for categorical data. Beginning with a sample of N {\displaystyle ~N~} items each
Multinomial_test
Statistical modeling method
log-normal data, instead the response variable is simply transformed using the logarithm function); when modeling categorical data, such as the choice of a given
Linear_regression
Set of probability measures
{\displaystyle \max } in the above expression). If X {\displaystyle X} is a categorical variable, then the credal set K ( X ) {\displaystyle K(X)} can be considered
Credal_set
Tree-based ensemble machine learning methods
accuracy of the base learner. Likewise in problems with multiple categorical variables. Boosting – Ensemble learning method Decision tree learning – Machine
Random_forest
Regression for more than two discrete outcomes
outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued
Multinomial logistic regression
Multinomial_logistic_regression
Statistical method
are small. It is specifically designed for testing whether two categorical variables are related or independent of each other. The correction modifies
Yates's correction for continuity
Yates's_correction_for_continuity
Statistical estimation method
outcomes: 4.1 The statistical model". Regression Models for Categorical Dependent Variables Using Stata, Second Edition. Stata Press. pp. 131–136. ISBN 978-1-59718011-5
Binary_regression
Method of data analysis
analysis for categorical data. Principal component analysis creates variables that are linear combinations of the original variables. The new variables have the
Principal_component_analysis
Concept in statistics
assess whether the factor variables (categorical variables) are additively related to the expected value of the response variable. It can be applied when
Tukey's_test_of_additivity
Statistical method
obviously when a categorical variable is coded as a collection of binary covariates. In this case, group lasso can ensure that all the variables encoding the
Lasso_(statistics)
Formal fallacy in statistical interpretation
dummy variable and the omitted variable Z {\displaystyle Z} is a categorical variable defining groups for each value it takes. The bias can be high enough
Ecological_fallacy
people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text
Data_analysis
Non-parametric regression technique
continuous and categorical data. MARS (like recursive partitioning) does automatic variable selection (meaning it includes important variables in the model
Multivariate adaptive regression spline
Multivariate_adaptive_regression_spline
Decision support tool
easy to interpret as a single decision tree. For data including categorical variables with different numbers of levels, information gain in decision trees
Decision_tree
Statistical property quantifying how much a collection of data is spread out
can be used to counteract linear frequency drift sensitivity. For categorical variables, it is less common to measure dispersion by a single number; see
Statistical_dispersion
Evaluates how likely it is that any difference between data sets arose by chance
the distribution of counts for two or more groups using the same categorical variable (e.g. choice of activity—college, military, employment, travel—of
Pearson's_chi-squared_test
Area of mathematical logic
isomorphism type. A theory that is both ω-categorical and uncountably categorical is called totally categorical. A key factor in the structure of the class
Model_theory
Type of logical argument that applies deductive reasoning
representing categorical statements (and statements that are not provided for in syllogism as well) by the use of quantifiers and variables. A noteworthy
Syllogism
Type of theory in mathematical logic
In mathematical logic, a theory is categorical if it has exactly one model (up to isomorphism). Such a theory can be viewed as defining its model, uniquely
Categorical_theory
Statistical technique
of a pair of nominal variables where each cell contains either a count or a zero value. If more than two categorical variables are to be summarized,
Correspondence_analysis
Form of longitudinal study
occurrence of a disease, births, a political attitude or any other categorical variable are collected after the events have taken place, and the subjects
Cohort_study
Statistical measure of inter-rater agreement
variance (ANCOVA)] MANOVA Degrees of freedom Categorical / multivariate / time-series / survival analysis Categorical Cohen's kappa Contingency table Graphical
Bennett, Alpert and Goldstein's S
Bennett,_Alpert_and_Goldstein's_S
Named set of data type values
R, a condition-name in COBOL, a status variable in the JOVIAL, an ordinal in PL/I, or a categorical variable in statistics. In some languages explicit
Enumerated_type
variance (ANCOVA)] MANOVA Degrees of freedom Categorical / multivariate / time-series / survival analysis Categorical Cohen's kappa Contingency table Graphical
Andres_and_Marzo's_delta
Number of occurrences in an experiment or study
categories are usually specified as consecutive, non-overlapping intervals of a variable. The categories (intervals) must be adjacent, and often are chosen to be
Frequency_(statistics)
Catastro of Ensenada – a census of part of Spain Categorical data Categorical distribution Categorical variable Cauchy distribution Cauchy–Schwarz inequality
List_of_statistics_articles
Form of statistical factor analysis
Victoria (2012). "When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under
Confirmatory_factor_analysis
Statistical framework for analyzing data from dyads
distinguish between conceptual distinguishability (whether a within-dyad categorical variable can label members) and empirical distinguishability (whether that
Actor–partner interdependence model
Actor–partner_interdependence_model
Bayesian nonparametric model of probability distributions
could for instance use this information to collect more data. For categorical variables, i.e., when X {\displaystyle \mathbb {X} } has a finite number of
Imprecise_Dirichlet_process
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