# Math 356H: Linear Statistical Models

Instructor: Michelle Boué
Email:
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: math@trentu.ca or cjohns@trentu.ca 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.

## Text

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.

## Prerequisites

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.

## Course Schedule

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.

## Course Evaluation

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.
Summary:
 Problem sets: 6 x 10% 60% Final exam 1 x 40% 40% 100%

## Course Outline

1. Simple Linear Regression
1. The simple linear regression model
2. Least squares estimation of the regression parameters
3. Inferences
1. Inferences concerning the slope parameter
2. Inferences concerning the intercept
3. Interval estimation of the mean
4. Prediction of new observations
4. Correlation
5. Remedial measures
2. Matrix Approach to Linear Regression
3. General Regression Models
1. Polynomial regression
2. General multiple regression
3. Diagnostics and remedial measures
4. Building the regression model
4. Analysis of Variance
1. Single-factor ANOVA
2. Multiple comparisons
3. Multifactor ANOVA
4. Non-parametric approach
5. Experimental Designs
1. Randomized block designs
2. Latin squares