Mathematics | Advanced Statistical Techniques 1
M467 | 26213 | Michael Trosset

This course will provide a careful study of linear regression,
balancing discussion of the statistical principles (e.g., the method
of least squares) that underlie regression methodology with
consideration of computational and diagnostic concerns. An important
theme will be the use of state-of-the-art graphical methods that
facilitate interpretation of regression analyses. Although no
previous knowledge of regression is assumed, the treatment of topics
such as weighted regression, lack of fit, polynomial regression, data
transformation, the analysis of residuals, outliers and influence,
and variable selection will be in sufficient depth that many students
may find this to be a useful second course on regression. More
generally, this would be an excellent second course in statistics for
undergraduates who anticipate jobs and/or graduate study in
quantitative disciplines.

Prerequisites: Students should have already taken at least one course
in statistics, have some facility with matrix algebra, and have some
experience in writing simple computer programs. (Note: these
requirements supersede the generic catalog requirements for M467of
M365 and M366; in particular, M366 is not a prerequisite for this
course.) If you are uncertain whether or not your background is
adequate, then please contact the instructor, Michael Trosset
, for more information and obtain his consent.


Applied Linear Regression, by Sanford Weisberg. Publisher: Wiley-
Interscience; 3 edition. ISBN: 0471663794

Applied Regression Including Computing and Graphics, by R. Dennis
Cook and Sanford Weisberg. Publisher: Wiley-Interscience. ISBN: