[R] Online course- Mixed Effects Models

[Peter C. Bruce]

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
courses at statistics.com