Math 356H: Linear Statistical Models
Winter 2008 (Reading Course)
Instructor: Michelle Boué Email:
email@example.com Office Phone: 748-1011 x7925 Office: Peter Gzowski College 332 (Enweying) Office hours:
Tuesday 11-11:50 am Wednesday 11-11:50 am Friday 10-10:50 am
Secretary: Carolyn Johns Email: firstname.lastname@example.org or email@example.com Office Phone: 748-1011 x7531 Fax: 748-1011 x1555 Office: Peter Gzowski College 342 (Enweying)
Course Topics and Objectives
Mathematics 356H provides an introduction to the study of linear statistical models for regression, analysis of variance and experimental designs. Extensive use of statistical software is made throughout the course.
Required: Probability and Statistics for Engineering and the Sciences, by Jay L Devore (Fifth or Sixth Edition), Duxbury.
- Neter, J. Wasserman, W., Kutner, M.H. (1990), Applied Linear Statistical Models, Irwin.
- Draper, N., Smith, H. (1981) Applied Regression Analysis, Wiley.
This course is intended for students who have completed MATH 256H. Previous specific computing experience is not required for those parts of the course involving computer-based analysis.
The course will be administered as a reading course. This means that there will be no scheduled lecture times. Students should read the materials assigned according to the schedule provided, and hand in assignments as scheduled. Any questions regarding the material must be addressed with the instructor during office hours. There will be one final exam during the final exam period.
Problem Sets: There will be six problem sets through the year. Each problem set will contribute 10% of the final mark.
Final Examination: There will be a final three-hour examination during the final examination period. The final examination will contribute 40% of the final mark.
Problem sets:   6 x 10% 60% Final exam   1 x 40% 40% 100%
- Simple Linear Regression
- The simple linear regression model
- Least squares estimation of the regression parameters
- Inferences concerning the slope parameter
- Inferences concerning the intercept
- Interval estimation of the mean
- Prediction of new observations
- Remedial measures
- Matrix Approach to Linear Regression
- General Regression Models
- Polynomial regression
- General multiple regression
- Diagnostics and remedial measures
- Building the regression model
- Analysis of Variance
- Single-factor ANOVA
- Multiple comparisons
- Multifactor ANOVA
- Non-parametric approach
- Experimental Designs
- Randomized block designs
- Latin squares
Academic dishonesty, which includes plagiarism and cheating, is an extremely serious academic offense and carries penalties varying from failure in an assignment to suspension from the University. Definitions, penalties, and procedures for dealing with plagiarism and cheating are set out in Trent University’s Academic Dishonesty Policy which is printed in the University Calendar.
Access to Instruction
It is Trent University’s intent to create an inclusive learning environment. If a student has a disability and/or health consideration and feels that he/she may need accommodations to succeed in this course, the student should contact the Disability Services Office (BL Suite 109, 748-1281, firstname.lastname@example.org) as soon as possible. Complete text can be found under Access to Instruction in the Academic Calendar.