Dr. Andrzej Galecki will present his online course "Mixed Effects Models with Applications" May 12 June 9 at statistics.com.
This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models (HLM). It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. Examples in R and SAS will be presented and discussed. Mixed models are a powerful class of models used for the analysis of correlated data such as clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies. A key feature of mixed models is that, by introducing random effects in addition to fixed effects, they allow you to address multiple sources of variation, e.g. in the longitudinal study they allow you to take into account both within- and between- subject variation. Prof. Galecki holds a joint appointment at the University of Michigan Schools of Public Health and Medicine, and is co-author of "Linear Mixed Models: A Practical Guide using Statistical Software" (forthcoming, CRC Press). Participants will interact with Dr. Galecki via a private discussion board; the course will require about 5-15 hours per week and there are no set hours when you must be online. Registration and details at http://www.statistics.com/content/courses/mixedmodels/index.html Peter Bruce [EMAIL PROTECTED] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
