The aim of this course is to provide an introduction to theory and application of regression and
analysis of variance. In particular,  we will cover estimation and testing theory for least squares fits
under general, Gauss-Markov, and related assumptions; diagnostic tools; prediction  and model selection
 in linear regression; and generalized linear models. The lectures will mainly focus on the theory  and provide
examples of applications.  The homework assignments will provide the opportunity to implement many
 of the methods on real data. See the syllabus for more information.

Linear algebra, Introductory Statistics and Probability.

All of the four books below are on reserve at Wendt library.

As our main text book, we will use

• Linear regression analysis by G.A.F. Seber and A.J. Lee (2nd edition).

For some of the topics that will be covered, you might also find the following book useful.

• Linear models: least squares and alternatives by C. R. Rao and H. Toutenburg.
  

In the last few weeks of the course, we will be using

• Generalized linear models by P. McCullagh and J.A. Nelder (2nd edition).
  

A good source on R/S is

• Modern Applied Statistics with S by W. N. Venables and B.D. Ripley (4th edition).
  

We will use R, a freely available implementation of S language, as our computing environment.
Discussion sessions will include basic information on related R functions. Tutorials are available  on the  R website.