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Fitting Generalized Linear Models
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| Organization: | Wolfram Research, Inc. |
| Department: | Kernel Technology |
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2005 Wolfram Technology Conference
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Champaign IL
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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.
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| GLMFitting.nb (2.3 MB) - Mathematica Notebook [for Mathematica 5.2] |
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