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  2. Univariate (statistics) - Wikipedia

    en.wikipedia.org/wiki/Univariate_(statistics)

    Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. [1]

  3. Bivariate analysis - Wikipedia

    en.wikipedia.org/wiki/Bivariate_analysis

    Simple linear regression is a statistical method used to model the linear relationship between an independent variable and a dependent variable.

  4. Bivariate data - Wikipedia

    en.wikipedia.org/wiki/Bivariate_data

    In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. [1] It is a specific but very common case of multivariate data.

  5. Univariate - Wikipedia

    en.wikipedia.org/wiki/Univariate

    In mathematics, a univariate object is an expression, equation, function or polynomial involving only one variable.Objects involving more than one variable are multivariate.

  6. Univariate distribution - Wikipedia

    en.wikipedia.org/wiki/Univariate_distribution

    Continuous uniform distribution. One of the simplest examples of a discrete univariate distribution is the discrete uniform distribution, where all elements of a finite set are equally likely.

  7. Analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_variance

    Analysis of variance (ANOVA) is a family of statistical methods used to compare the means of two or more groups by analyzing variance. Specifically, ANOVA compares the amount of variation between the group means to the amount of variation within each group.

  8. Two-way analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Two-way_analysis_of_variance

    In statistics, the two-way analysis of variance (ANOVA) is an extension of the one-way ANOVA that examines the influence of two different categorical independent variables on one continuous dependent variable.

  9. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    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]