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  2. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    The extension to multiple and/or vector-valued predictor variables (denoted with a capital X) is known as multiple linear regression, also known as multivariable linear regression (not to be confused with multivariate linear regression). [10] Multiple linear regression is a generalization of simple linear regression to the case of more than one ...

  3. Model output statistics - Wikipedia

    en.wikipedia.org/wiki/Model_output_statistics

    In weather forecasting, model output statistics (MOS) is a multiple linear regression technique in which predictands, often near-surface quantities (such as two-meter-above-ground-level air temperature, horizontal visibility, and wind direction, speed and gusts), are related statistically to one or more predictors.

  4. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...

  5. Bayesian multivariate linear regression - Wikipedia

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

    The classical, frequentists linear least squares solution is to simply estimate the matrix of regression coefficients ^ using the Moore-Penrose pseudoinverse: ^ = (). To obtain the Bayesian solution, we need to specify the conditional likelihood and then find the appropriate conjugate prior.

  6. 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]

  7. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression. [1]

  8. Numerical methods for linear least squares - Wikipedia

    en.wikipedia.org/wiki/Numerical_methods_for...

    It can therefore be important that considerations of computation efficiency for such problems extend to all of the auxiliary quantities required for such analyses, and are not restricted to the formal solution of the linear least squares problem. Matrix calculations, like any other, are affected by rounding errors. An early summary of these ...

  9. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much ...