Difference between revisions of "Generalized linear model"
Jump to navigation
Jump to search
Conservative (talk | contribs) (Created page with "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,...") |
m (The equation is clearer with subscripts) |
||
| Line 3: | Line 3: | ||
"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 (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 = | + | 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> |
| − | In this [[equation]] | + | In this [[equation]] b<sub>0</sub> is the [[regression analysis|regression]] [[coefficient]] for the intercept and the b<sub>i</sub> values are the regression coefficients (for variables 1 through k) computed from the [[data]]."<ref>http://www.statsoft.com/textbook/generalized-linear-models/</ref> |
== See also == | == See also == | ||
Revision as of 16:37, March 27, 2011
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]