The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In...

generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to...

term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression models and the...

In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory...

log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which...

These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These...

discuss mainly linear mixed-effects models rather than generalized linear mixed models or nonlinear mixed-effects models. Linear mixed models (LMMs) are statistical...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). Regression analysis is primarily used...

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems...

involves modeling the error as a linear combination of error terms occurring contemporaneously and at various times in the past. The model is usually...

statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random...

using similar techniques. When viewed in the generalized linear model framework, the probit model employs a probit link function. It is most often estimated...

In statistics, a linear probability model (LPM) is a special case of a binary regression model. Here the dependent variable for each observation takes...

In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent...

of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs). In particular...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances...

explanatory variable General linear model for multivariate predictands Generalised linear model for non-normal distributions Bayesian linear regression, where...

A partially linear model is a form of semiparametric model, since it contains parametric and nonparametric elements. Application of the least squares estimators...

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...

Google books Dawes, Robyn M. (1979). "The robust beauty of improper linear models in decision making". American Psychologist, volume 34, pages 571-582...

Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable...

The linear no-threshold model (LNT) is a dose-response model used in radiation protection to estimate stochastic health effects such as radiation-induced...

modeling see least squares and non-linear least squares. The assumption underlying this procedure is that the model can be approximated by a linear function...

squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the...

general linear model, containing the group and the covariates, and substitute Y ¯ {\textstyle {\bar {Y}}} with the predictions of the general linear model...

that it is not constrained by any functional form, such as the linearity imposed by linear regression, as long as the function is monotonic increasing....

discriminate between the fixed and the random effects models. Consider the linear unobserved effects model for N {\displaystyle N} observations and T {\displaystyle...

lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of...