MATH-408 / 5 credits

Teacher: Davison Anthony Christopher

Language: English


Summary

An advanced course on regression modelling.

Content

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.

 

Learning Prerequisites

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

Resources

Bibliography

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

Ressources en bibliothèque

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

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22