enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    A model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. [1] This term is distinct from multivariate linear regression, which predicts multiple correlated dependent variables rather than a single dependent variable. [2]

  3. 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 ...

  4. Bivariate analysis - Wikipedia

    en.wikipedia.org/wiki/Bivariate_analysis

    The least squares regression line is a method in simple linear regression for modeling the linear relationship between two variables, and it serves as a tool for making predictions based on new values of the independent variable. The calculation is based on the method of the least squares criterion. The goal is to minimize the sum of the ...

  5. Design matrix - Wikipedia

    en.wikipedia.org/wiki/Design_matrix

    The design matrix contains data on the independent variables (also called explanatory variables), in a statistical model that is intended to explain observed data on a response variable (often called a dependent variable). The theory relating to such models uses the design matrix as input to some linear algebra : see for example linear regression.

  6. Vector generalized linear model - Wikipedia

    en.wikipedia.org/wiki/Vector_generalized_linear...

    It relates the jth parameter to the explanatory variables, and is expressed as linear combinations (thus, "linear") of unknown parameters , i.e., of regression coefficients (). The j th parameter, θ j {\displaystyle \theta _{j}} , of the distribution depends on the independent variables, x , {\displaystyle {\boldsymbol {x}},} through

  7. Linear predictor function - Wikipedia

    en.wikipedia.org/wiki/Linear_predictor_function

    For example, a four-way discrete variable of blood type with the possible values "A, B, AB, O" would be converted to separate two-way dummy variables, "is-A, is-B, is-AB, is-O", where only one of them has the value 1 and all the rest have the value 0. This allows for separate regression coefficients to be matched for each possible value of the ...

  8. Errors-in-variables model - Wikipedia

    en.wikipedia.org/wiki/Errors-in-variables_model

    Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.

  9. Least-angle regression - Wikipedia

    en.wikipedia.org/wiki/Least-angle_regression

    In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. [1] Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates.