General
Description: Introduction to Statistical Modeling (ISM) is a new
statistics course based on the idea that modeling provides a unifying
framework for much of introductory statistics, and that students can
learn the important concepts of statistics best by mastering "advanced"
statistical techniques relating to modeling. The course covers
the
foundations of
statistics with a strong emphasis on constructing models from data.
Topics include descriptive statistics, probability (including
conditional probabilities and Bayes rule), multiple regression,
multiway analysis of variance, and logistic regression. The
pre-requisite at Macalester is Applied Calculus (Math 135 --- our introductory-level course).
Macalester Students and Faculty :
All the course content for ISM is now available through the Moodle
system. Contact kaplan@macalester.edu if you would like the
registration key.
Off-campus: If you are off-campus, write to
Danny Kaplan
to get copies of the materials for the course. [Many of these are not
posted on the web because they are for instructors, not students.]
Available materials.
- A prospectus for the course, oriented toward instructors, presented at the Bioquest "Exploring Complex Data Sets" meeting in June 2006.
- Slides from presentations at the Joint Statistical Meetings:
- A manual for the essential computer skills for ISM using the R statistical software package.
- A large collection of exercises used in the course. Many of these can be automatically graded using the AcroScore system.
- Draft chapters of a textbook for the course.
Some important future events:
May 2007 -- A workshop in conjunction with the
USCOTS meeting to introduce faculty to the course.
Summer 2008 -- A
one-week summer workshop to introduce faculty to the course.
If you think you would like to offer a similar course at your
institution, we would like to hear from you.
Acknowledgements:
Initial development of ISM was carried out with support of a grant from the Howard Hughes Medical Institute.
Ongoing development and dissemination is being supported by a grant from the Keck Foundation.