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  2. 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. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to ...

  3. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    The normal equations are ... a researcher is building a linear regression model using a dataset that contains 1000 patients ... Multivariate adaptive regression spline;

  4. General linear model - Wikipedia

    en.wikipedia.org/wiki/General_linear_model

    The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model. The various multiple linear regression models may be compactly written as [1]

  5. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    This term is distinct from multivariate linear regression, which predicts multiple correlated dependent variables rather than a single dependent variable. [2] In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data.

  6. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/.../Multinomial_logistic_regression

    The formulation of binary logistic regression as a log-linear model can be directly extended to multi-way regression. That is, we model the logarithm of the probability of seeing a given output using the linear predictor as well as an additional normalization factor, the logarithm of the partition function:

  7. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    The above matrix equations explain the behavior of polynomial regression well. However, to physically implement polynomial regression for a set of xy point pairs, more detail is useful. The below matrix equations for polynomial coefficients are expanded from regression theory without derivation and easily implemented. [6] [7] [8]

  8. Multilevel modeling for repeated measures - Wikipedia

    en.wikipedia.org/wiki/Multilevel_Modeling_for...

    Fixed Effects: Fixed regression coefficients may be obtained for an overall equation that represents how, averaging across subjects, the subjects change over time. Random Effects: Random effects are the variance components that arise from measuring the relationship of the predictors to Y for each subject separately. These variance components ...

  9. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    The basic idea of logistic regression is to use the mechanism already developed for linear regression by modeling the probability p i using a linear predictor function, i.e. a linear combination of the explanatory variables and a set of regression coefficients that are specific to the model at hand but the same for all trials.