Math 356H: Linear Statistical Models
MATH 356H course materials for the Winter 2008 term can be found here.
Instructor: Michelle Boué Email:
firstname.lastname@example.org Office Phone: 748-1011 x7925 Office: Peter Gzowski College 332 (Enweying)
Secretary: Carolyn Johns Email: email@example.com or firstname.lastname@example.org 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.
WebCTAny material relevant to the course as well as updated grades will be available on WebCT.
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.
Calculators:Due to the considerable amount of numerical work involved in this course, students should possess a calculator with built-in statistical function keys.
There will be three lecture hours per week as indicated in the Class timetable. Students are responsible for all material covered in lectures and for all announcements made in lecture hours.
Problem Sets: There will be five problem sets through the year. Each problem set will contribute 8% of the final mark.
Midterm examinations: There will be two in-class exams. Each exam will contribute 15% of the final mark.
Final Examination: There will be a final three-hour examination during the final examination period. The final examination will contribute 30% of the final mark.
Problem sets:   5 x 8% 40% Midterm exams   2 x 15% 30% Final exam   1 x 30% 30% 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
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Access to Instruction
It is Trent University's intent to create an inclusive learning environment. If a student has a disability and/or healh 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, email@example.com) as soon as possible. Complete text can be found under Access to Instruction in the Academic Calendar.