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Fitting Generalized Linear Models

Darren Glosemeyer
Organization: Wolfram Research, Inc.
Department: Kernel Technology

2005 Wolfram Technology Conference
Conference location

Champaign IL

The linear regression model fits a response variable to a linear combination of predictor variables, assuming the measurement error in the response follows a normal distribution. Generalized linear models generalize the linear regression model to cases where the response variable is modeled by a smooth function of a linear combination of predictor variables, and the response variable may be assumed to follow a distribution other than the normal distribution. Some common generalized linear model structures include loglinear models for count data, logistic regression, and probit regression. This talk will explore fitting generalized linear models via examples of some common generalized linear model structures.

*Mathematics > Probability and Statistics
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GLMFitting.nb (2.3 MB) - Mathematica Notebook [for Mathematica 5.2]