Difference between revisions of "Generalized linear model"
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The website statsoft.com offers this explanation of the '''generalized linear model''': | 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 [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|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 | + | "The Generalized Linear Model (GLZ) is a generalization of the [[General linear model|general linear model]] (see, e.g., the General Linear Models, [[Multiple Regression]], and [[Analysis of variance|ANOVA]]/[[Multivariate Analysis of Variance|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 = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub> | Y = b<sub>0</sub> + b<sub>1</sub>X<sub>1</sub> + b<sub>2</sub>X<sub>2</sub> + ... + b<sub>k</sub>X<sub>k</sub> | ||
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{{reflist|2}} | {{reflist|2}} | ||
| − | [[Category: Statistics]] | + | [[Category:Statistics]] |
Latest revision as of 07:43, February 9, 2019
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.