Everything about General Linear Model totally explained
The
general linear model (GLM) is a statistical
linear model.
It may be written as
»
where
Y is a matrix with series of multivariate measurements,
X is a matrix that might be a
design matrix,
B is a matrix containing parameters that are usually to be estimated and
U is a matrix containing
errors or noise.
The residual is usually assumed to follow a
multivariate normal distribution. If the residual isn't a
multivariate normal distribution,
generalized linear models may be used to relax assumptions about
Y and
U.
The general linear model incorporates a number of different statistical models:
ANOVA,
ANCOVA,
MANOVA,
MANCOVA, ordinary
linear regression,
t-test and
F-test. If there's only one column in
Y (for example, one dependent variable) then the model can also be referred to as the
multiple regression model (multiple linear regression).
Hypothesis tests with the general linear model can be made in two ways:
multivariate and
mass-univariate.
Applications
An application of the general linear model appears in the analysis of neuroimages where
Y contains data from brain scanners,
X contains experimental design variables and confounds. It is usually tested in a mass-univariate way and is often referred to as
statistical parametric mapping.
Further Information
Get more info on 'General Linear Model'.
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