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  2. Jamovi - Wikipedia

    en.wikipedia.org/wiki/Jamovi

    jamovi is an open source graphical user interface for the R programming language. [3] It is used in statistical research, especially as a tool for ANOVA (analysis of variance) and to understand statistical inference. [4] [5] It also can be used for linear regression, [6] mixed models and Bayesian models. [7]

  3. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    This solution closely resembles that of standard linear regression, with an extra term . If the assumptions of OLS regression hold, the solution w = ( X T X ) − 1 X T y {\displaystyle w=\left(X^{\mathsf {T}}X\right)^{-1}X^{\mathsf {T}}y} , with λ = 0 {\displaystyle \lambda =0} , is an unbiased estimator, and is the minimum-variance linear ...

  4. Repeated median regression - Wikipedia

    en.wikipedia.org/wiki/Repeated_median_regression

    In robust statistics, repeated median regression, also known as the repeated median estimator, is a robust linear regression algorithm. The estimator has a breakdown point of 50%. [ 1 ] Although it is equivariant under scaling, or under linear transformations of either its explanatory variable or its response variable, it is not under affine ...

  5. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Linear regression plays an important role in the subfield of artificial intelligence known as machine learning. The linear regression algorithm is one of the fundamental supervised machine-learning algorithms due to its relative simplicity and well-known properties. [34]

  6. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.

  7. Elastic net regularization - Wikipedia

    en.wikipedia.org/wiki/Elastic_net_regularization

    In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L 1 and L 2 penalties of the lasso and ridge methods. Nevertheless, elastic net regularization is typically more accurate than both methods with regard to reconstruction. [1]

  8. R (programming language) - Wikipedia

    en.wikipedia.org/wiki/R_(programming_language)

    R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.

  9. Bayesian linear regression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_linear_regression

    Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...