lecture_14 - Generalized Linear Models Lecture 14 ILRST 212...

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    Generalized Linear Models Lecture 14 ILRST 212
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       Benefits of good fitting model The structural form of the model describes the patterns of  the association and interaction. Inferences for model parameters help us evaluate which  explanatory variables affect response, while controlling  effects of possible confounding variable. The sizes of the estimated model parameters determine  the strength and importance of the effects. The model’s predicted values smooth the data and provide  improved estimates of the mean of the response  distribution.
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    Models VS two and three way tables Models can handle more complicated situations such as  analyzing simultaneously the effects of several explanatory  variables. Model building paradigm focuses on estimating parameters  that describe the effects, which is more informative than  mere significance testing. The explanatory variables in the models can be continuous  or categorical or both types.
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      Generalized Linear Model Generalized Linear model is a generalization of the general  linear model ( eg. Multiple regression,ANOVA). Many relationships cannot be adequately summarized by a  simple linear equation, for two major reasons: Distribution of dependent variable Link function
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        Generalized Linear Model Special case is logistic regression model.
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