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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. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    All major statistical software packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some spreadsheet applications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression ...

  4. Commonality analysis - Wikipedia

    en.wikipedia.org/wiki/Commonality_analysis

    Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R 2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients.

  5. General linear model - Wikipedia

    en.wikipedia.org/wiki/General_linear_model

    The general linear model incorporates a number of different statistical models: ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression to the case of more than one dependent variable.

  6. Scheffé's method - Wikipedia

    en.wikipedia.org/wiki/Scheffé's_method

    It is particularly useful in analysis of variance (a special case of regression analysis), and in constructing simultaneous confidence bands for regressions involving basis functions. Scheffé's method is a single-step multiple comparison procedure which applies to the set of estimates of all possible contrasts among the factor level means, not ...

  7. Multilevel regression with poststratification - Wikipedia

    en.wikipedia.org/wiki/Multilevel_regression_with...

    The multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression) : The multilevel regression model specifies a linear predictor for the mean μ Y {\displaystyle \mu _{Y}} , or the logit transform of the mean in the case of a binary outcome, in poststratification cell j ...

  8. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.

  9. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    In other words, a simple linear regression model might, for example, predict that a given randomly sampled person in Seattle would have an average yearly income $10,000 higher than a similar person in Mobile, Alabama. However, it would also predict, for example, that a white person might have an average income $7,000 above a black person, and a ...