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  2. Bivariate analysis - Wikipedia

    en.wikipedia.org/wiki/Bivariate_analysis

    Graphs that are appropriate for bivariate analysis depend on the type of variable. For two continuous variables, a scatterplot is a common graph. When one variable is categorical and the other continuous, a box plot is common and when both are categorical a mosaic plot is common. These graphs are part of descriptive statistics.

  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. The association can be studied via a tabular or graphical display, or via sample statistics which might be used for inference.

  4. Binary function - Wikipedia

    en.wikipedia.org/wiki/Binary_function

    In mathematics, a binary function (also called bivariate function, or function of two variables) is a function that takes two inputs. Precisely stated, a function f {\displaystyle f} is binary if there exists sets X , Y , Z {\displaystyle X,Y,Z} such that

  5. Multivariate statistics - Wikipedia

    en.wikipedia.org/wiki/Multivariate_statistics

    The extracted variables are known as latent variables or factors; each one may be supposed to account for covariation in a group of observed variables. Canonical correlation analysis finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate [3] correlation.

  6. Bivariate - Wikipedia

    en.wikipedia.org/wiki/Bivariate

    Bivariate data, that shows the relationship between two variables Bivariate analysis , statistical analysis of two variables Bivariate distribution, a joint probability distribution for two variables

  7. List of probability distributions - Wikipedia

    en.wikipedia.org/wiki/List_of_probability...

    This does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in the same formalism. The discrete uniform distribution, where all elements of a finite set are equally likely. This is the theoretical distribution model for a balanced coin, an unbiased ...

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