In statistical modeling of data observations (), two types of variables are usually defined. One is the response variable or variate, usually denoted by , and the other is the explanatory variable or covariate . 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 is a continuous random variable
2. the response variable can be expressed as a linear combination of functions , of the explanatory variables, plus a random error term :
   
   The portion of without the error term is known as the systematic component of .
3. the error component and the systematic component are independent
4. random error variables for the observations are iid normal with mean 0 and variance

Remarks

• Conditioning on the explanatory variables, the random variables corresponding to the individual responses are independent, normally distributed, with mean
  
  and variance .
• 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 has a normal distribution is dropped.