MATH-408 / 5 credits

Teacher: Davison Anthony Christopher

Language: English

## Summary

An advanced course on regression modelling.

## Keywords

Binary response; Count data; Deviance; EM algorithm; Iterative weighted least squares algorithm; Lasso; Logistic regression; Mixed model; Overdispersion; Poisson distribution; Quasi-likelihood; Random effects; Ridge regression.

## Required courses

Knowledge of basic probability and statistics, at, for example, the levels of MATH-240 and MATH-230

Linear models (MATH-341) or equivalent

## Important concepts to start the course

Linear regression; likelihood inference; R

## Learning Outcomes

By the end of the course, the student must be able to:

• Develop theoretical elements needed in regression analysis
• Apply the statistical package R to the analysis of data
• Assess / Evaluate the quality of a model fitted to regression data, and suggest improvements
• Choose a suitable regression model

## Transversal skills

• Demonstrate a capacity for creativity.
• Demonstrate the capacity for critical thinking
• Write a scientific or technical report.

## Teaching methods

Ex cathedra lectures; homework both theoretical and practical; mini-project

## Expected student activities

Attending lectures; solving theoretical problems; solving applied problems using statistical software

## Assessment methods

Written final exam; mini-project

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

## Supervision

 Office hours No Assistants Yes Forum Yes

## Bibliography

Davison, A. C. (2003) Statistical Models.  Cambridge University Press.

## In the programs

• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Modern regression methods
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Modern regression methods
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Modern regression methods
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Modern regression methods
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Modern regression methods
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 9-10 10-11 11-12 12-13 13-14 MAA110 14-15 CM1104 15-16 16-17 17-18 18-19 19-20 20-21 21-22

Wednesday, 14h - 16h: Lecture CM1104

Friday, 13h - 15h: Exercise, TP MAA110