1.	To bring students to an understanding of how to use 
regression analysis in academic behavioral research.
2.	To advance students' ability to think critically about 
their regression analysis, and the analytical work of others.
3.	To give students the understanding of regression 
analysis that they will need as foundational knowledge for 
future statistical study.

- Review of basic statistics and correlation
- Tests of significance
- Assumptions and testing of assumptions
- Simple linear regression
- Multiple regression
- Diagnostics
- Residual analysis
- Influence analysis
- Variance partitioning
- Shrinkage
- Multicollinearity
- Modeling, including curvilinear analysis
- Categorical independent variables
- Interactions
- Power analysis
- Examination of regression usage in academic research
- Other topics as time allows

Except where specified, all projects/assignments in this course 
are individual projects/assignments.  This means that you are 
not to collaborate in any way with other people to complete 
your project/assignments.  It is acceptable to ask other people
for instructional help, but the actual project/assignment must be 
entirely your own individual work.  This policy is designed to 
ensure that each student learns how to do her own work and to 
protect those students who do their own work.  Collaborative 
efforts on individual work are violations of academic honesty, 
and will be treated as such with actions such as referral to 
the appropriate university body, a failing status for the 
project/assignment and/or course, etc.  Please refer to the 
University's academic honesty policy for more details.

Additionally, it is expected that all data sources, formulae, 
benchmarks, quoted materials, findings from previous studies 
(including the student's own previous work), and all other 
citable components of submitted work will cite the appropriate 
references.  Failure to do so will be treated as a violation of 
academic honesty, and will be subject to such actions as those 
mentioned above.