Difference between revisions of "Generalized linear model"
m (Reverted edits by Dolphin11 (talk) to last revision by FrederickT3) |
DavidB4-bot (talk | contribs) (→Notes: clean up & uniformity) |
||
| Line 16: | Line 16: | ||
{{reflist|2}} | {{reflist|2}} | ||
| − | [[Category: Statistics]] | + | [[Category:Statistics]] |
Revision as of 13:20, July 13, 2016
The website statsoft.com offers this explanation of the generalized linear model:
"The Generalized Linear Model (GLZ) is a generalization of the general linear model (see, e.g., the General Linear Models, Multiple Regression, and ANOVA/MANOVA topics). In its simplest form, a linear model specifies the (linear) relationship between a dependent (or response) variable Y, and a set of predictor variables, the X's, so that
Y = b0 + b1X1 + b2X2 + ... + bkXk
In this equation b0 is the regression coefficient for the intercept and the bi values are the regression coefficients (for variables 1 through k) computed from the data."[1]
Whereas in classical linear models the variable Y is assumed to follow a normal distribution, generalized linear models consider more general distributions, such as a Poisson or a multinomial distribution, and are therefore applicable to a wider field of problems.