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  2. 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.

  3. 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.

  4. Cochran–Mantel–Haenszel statistics - Wikipedia

    en.wikipedia.org/wiki/Cochran–Mantel–Haenszel...

    In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data.It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [1]

  5. Contingency table - Wikipedia

    en.wikipedia.org/wiki/Contingency_table

    The numbers of the males, females, and right- and left-handed individuals are called marginal totals.The grand total (the total number of individuals represented in the contingency table) is the number in the bottom right corner.

  6. Multivariate statistics - Wikipedia

    en.wikipedia.org/wiki/Multivariate_statistics

    Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables.

  7. Bivariate - Wikipedia

    en.wikipedia.org/wiki/Bivariate

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  8. Nonparametric statistics - Wikipedia

    en.wikipedia.org/wiki/Nonparametric_statistics

    Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as in parametric statistics. [1]

  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]