Course is intended to provide an introduction to both fixed- and
mixed-effects nonlinear regression models with emphasis on the
practical aspects of the use of these models in the analysis of
real data.  Fixed-effects nonlinear regression is given only 
brief coverage in existing courses and mixed-effects nonlinear
models are not taught at all.  This limited coverage is despite
these models common and increasing use in modeling biological
growth (e.g., in Forest Biometrics), chemical kinetics and
dynamics (e.g., in Pharmaceutical Sciences), and other processes.
In this course students will be introduced to the general classes
of normal error fixed- and mixed-effects nonlinear models.  They 
will learn practical aspects of how to fit, interpret and make
inferences from these models, and they will study the important
special cases of these models that are most often encountered in
applications.

Review of linear regression.  Fixed-effects nonlinear models
(NLMs).  Nonlinear least-squares.  Computational methods and
other practical considerations.  Growth models.  Compartmental
models.  Nonlinear mixed-effects models (NLMMs).  Methods of
estimation and inference in NLMMs.  Software for NLMs and
NLMMs.