Regression analysis plays a central role in statistics being one of its
most powerful and commonly used techniques.
Regression analysis deals with problems of finding appropriate models to
represent relationships between a response variable and a set of explanatory
variables based on data collected from a series of experiments. These models
are used to represent existing data and also to predict new observations.
The basic regression models are linear ones.
Although they are the simplest and (hence) most well studied models, they
nevertheless do work in numerious problems.
Sometimes even for non-linear models
it is possible to transfer the original non-linear model to a linear one after
certain transformations of variables; in some other cases linearization of
complex non-linear models may be used.
In this course we'll try to understand
how linear models work and when it is possible to use them efficiently.
The course assumes an extensive use of computer. There are no limitations
on using various statistical packages and software for this course, although
the data-examples considered in the class will be R-``oriented".
Installation instructions and manuals for R can be found on the
R Home page .
The following R based books may be helpful for this course: