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Generalized linear model (view source)
Revision as of 19:28, January 3, 2012
, 19:28, January 3, 2012no edit summary
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>
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/ Generalized Linear Models]</ref>
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 coefficien#REDIRECT [[Insert redirect]]ts (for variables 1 through k) computed from the [[data]]."<ref>[http://www.statsoft.com/textbook/generalized-linear-models/ Generalized Linear Models]</ref>
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.
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.