general linear model
In statistical modeling of $N$ data observations ($$), two types of variables are usually defined. One is the response variable or variate, usually denoted by $Y$, and the other is the explanatory variable or covariate $X$. While there is only one response variable, there may be one or more than one explanatory variables. The response variable is considered random, where as the explanatory variable(s) may or may not be random.
Based on the above setup, a general linear model, or normal linear model, is a statistical model with the following assumptions^{}:

1.
the response variable $Y$ is a continuous random variable

2.
the response variable $Y$ can be expressed as a linear combination^{} of functions ${z}_{i}(\text{\mathbf{X}})$, of the explanatory variables, plus a random error term $\epsilon $:
$$Y={\beta}_{0}{z}_{0}(\text{\mathbf{X}})+\mathrm{\cdots}+{\beta}_{k}{z}_{k}(\text{\mathbf{X}})+\epsilon .$$ The portion of $Y$ without the error term is known as the systematic component^{} of $Y$.

3.
the error component and the systematic component are independent^{}

4.
random error variables ${\epsilon}_{i}$ for the $N$ observations are iid normal with mean 0 and variance^{} ${\sigma}^{2}$
Remarks

•
Conditioning on the explanatory variables, the random variables ${Y}_{i}$ corresponding to the individual responses are independent, normally distributed, with mean
$$\mu =\mathrm{E}[Y\mid \text{\mathbf{X}}=\bm{x}]={\beta}_{0}{z}_{0}(\text{\mathbf{X}})+\mathrm{\cdots}+{\beta}_{k}{z}_{k}(\text{\mathbf{X}})$$ and variance ${\sigma}^{2}$.

•
A linear regression model is a special case of the general linear model where all explanatory variables are assumed to be continuous^{}.

•
Analysis of variance model, or ANOVA, is another special case of the general linear model, where all of the explantory variables are categorical in nature (for example, gender, marital status, etc..).

•
Analysis of covariance, or ANCOVA, sits between a linear regression model and the ANOVA, where some of the explanatory variables are continuous and some are categorical.

•
The general linear model is a special case of the generalized linear model, where the assumption that the response variable $Y$ has a normal distribution^{} is dropped.
Title  general linear model 
Canonical name  GeneralLinearModel 
Date of creation  20130322 14:31:23 
Last modified on  20130322 14:31:23 
Owner  CWoo (3771) 
Last modified by  CWoo (3771) 
Numerical id  6 
Author  CWoo (3771) 
Entry type  Definition 
Classification  msc 62J10 
Classification  msc 62J05 
Synonym  normal linear model 
Defines  analysis of variance 
Defines  ANOVA 
Defines  analysis of covariance 
Defines  ANCOVA 