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  2. Multinomial probit - Wikipedia

    en.wikipedia.org/wiki/Multinomial_probit

    Some examples: The observed outcomes might be "has disease A, has disease B, has disease C, has none of the diseases" for a set of rare diseases with similar symptoms, and the explanatory variables might be characteristics of the patients thought to be pertinent (sex, race, age, blood pressure , body-mass index , presence or absence of various ...

  3. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    For example, a researcher is building a linear regression model using a dataset that contains 1000 patients (). If the researcher decides that five observations are needed to precisely define a straight line ( m {\displaystyle m} ), then the maximum number of independent variables ( n {\displaystyle n} ) the model can support is 4, because

  4. Multivariate statistics - Wikipedia

    en.wikipedia.org/wiki/Multivariate_statistics

    Certain types of problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics because the analysis is dealt with by considering the (univariate) conditional distribution of a single outcome variable given the other variables.

  5. Anscombe's quartet - Wikipedia

    en.wikipedia.org/wiki/Anscombe's_quartet

    The calculated regression is offset by the one outlier, which exerts enough influence to lower the correlation coefficient from 1 to 0.816. Finally, the fourth graph (bottom right) shows an example when one high-leverage point is enough to produce a high correlation coefficient, even though the other data points do not indicate any relationship ...

  6. Unit-weighted regression - Wikipedia

    en.wikipedia.org/wiki/Unit-weighted_regression

    An example may clarify how unit weights can be useful in practice. Brenna Bry and colleagues (1982) addressed the question of what causes drug use in adolescents. Previous research had made use of multiple regression; with this method, it is natural to look for the best predictor, the one with the highest beta weight.

  7. Bayesian multivariate linear regression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_multivariate...

    In statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random variables rather than a single scalar random variable.

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