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Thursday, February 15
SC4 A Variety of Mixed Models: Linear, Generalized Linear, and Nonlinear Thu, Feb 15, 8:00 AM - 12:00 PM
Salon E
Instructor(s): David A. Dickey, NC State University
The MIXED procedure in SAS, for example, correctly handles linear models that have multiple sources of random effects such as random town to town, store to store, and aisle to aisle variation in sales. Associated fixed effects might be product price, color of packaging and amount spent on advertising. The talk begins with a checklist for deciding when to treat effects as random versus fixed and follows with a series of examples. When the response variable is not normal, for example with a binary or Poisson response, additional complexities arise. Models with such non normal responses are often analyzed by assuming that some transformation, or link function, of the expected value of Y results in a linear model with fixed and random effects. We are then in the generalized linear mixed model setting. It may be that a model cannot be linearized by a transformation, thus making it a nonnlinear model. If random effects are involved the model is referred to as a nonlinear mixed model. With a minimal amount of theory and an emphasis on examples, these types of models will be explained and illustrated. SAS will be used but the ideas and interpretation are software independent.