Syllabus

Instructor 1

Instructor 2

Course details

Course description

The objective of this course is to provide students with the basics of statistical inference, as well as statistical tools for data modeling in a regression framework. The theory behind the statistical models will be reviewed with an emphasis on data applications in management.

The course will focus on regression models, including linear models, generalized linear models and models for longitudinal data with random effects and correlation structures. For each of these models, basic inference principles will be covered, notably hypothesis testing and estimation procedures.

  • Basic principles in inference and statistical modeling
  • Linear models
  • Generalized linear models
  • Models for longitudinal data and correlated data
  • Introduction to survival analysis

Learning objectives

The course learning objectives are listed here.

Prerequisites

The course is of an overview course. Students should have basic training in statistics at the level of Introductory Statistics with Randomization and Simulation (Chapters 1–4, excluding special topics).

Target audience

Statistical modelling is a compulsory course of the M.Sc. in Data Science and Business Analytics (Supervised Project stream) and an optional course for the Thesis stream. Please note this course is not credited in the “Intelligence d’affaires” specialization: the course is incompatible with MATH606019(A).

Programming

The course uses the R programming language. If you have never programmed before, you may find there is a steep learning curve. R has a vibrant user community, so most routines of interest have been contributed by other users and you can get support through help forums. You may want to look at Douglas et al. (2024) An introduction to R, Julia Palms’ Introduction to R, the free CRAN manuals or similar introductions, or online courses, to brush up the basics of the programming language.

Follow the instructions to install R.

Evaluations and grades

Your final grade will be based on

  • a three part group project, with due dates on Friday October 11th, November 8th and December 6th.
  • a midterm evaluation taking place on Tuesday, October 29th, from 12:00-15:00, and
  • a final examination which will take place on Thursday, December 12th, from 13:30-16:30.

Students can bring in one-sided crib-sheet (letter paper) for the midterm, and a two-sided one for the final.

Team work counts towards your final grade only if you score more than 50% on individual evaluations.

Assignment Points
Course project (3 x 10 pt) 30
Midterm (30 pt) 30
Final examination (40 pt) 40
Total 100

Course content

Below is a tentative schedule for the semester.

  1. Introduction
    • Syllabus
    • Motivating examples
    • Review
    • Statistical inference (bhypothesis testing)
  2. Linear models
    • Parametrizations
    • Parameter interpretation
    • Coefficient of determination
  3. Likelihood based inference
    • Likelihood
    • Maximum likelihood estimation
    • Hypothesis tests
  4. Linear models
    • Predictions
    • Interactions
    • Collinearity
  5. Diagnostics for linear model hypotheses
    • Linear model hypotheses
    • Graphical diagnostics
    • Extensions and remedies
  6. Generalized linear models
    • Structure of GLMs
    • Poisson regression
    • Contingency tables
    • Overdispersion

Intellectual integrity and generative artificial intelligence

Please don’t cheat! The official policy lists the school rules regarding plagiarism and academic integrity.

The midterm and final are closed book exams. Students can use generative artificial intelligence (AI) tools for the project to improve and edit the deliverables (either code or text), but only as a proof-reading tool. That is, the first draft of the text must be student’s original work. Any use of generative AI must be cited adequately and students should provide their work with

  1. details about the tool used (name and version),
  2. the list of prompts,
  3. a copy of the original draft or code,
  4. a description of any modification to the proposed output,
  5. a short reflection on the usage of generative AI.

Student services

Students with special needs should feel free to approach me so we can best discuss accommodations. Do check out HEC Montréal’s disabled students and psychological support services.

Harassment and sexual violence

The Center for Harassment Intervention (BIMH) is the unique access point for all members of the community subject to harassment or sexual violence. You can reach them at 514 343-7020 or by email at harcelement@hec.ca from Monday until Friday, from 8:30 until 4:30pm.

If you are in an emergency situation or fear for your safety, call emergency services at 911, followed by HEC Montréal security services at 514 340-6611.

Check the school official policy on these matters for more details.

Family policy

HEC now has an official family policy, but the following guidelines reflect my own beliefs and commitments towards parent students1

  1. Babies are welcome in class as often as necessary for support feeding relationship.
  2. You are welcome to bring your child to class in order to cover unforeseeable gaps in childcare.
  3. If you come with babies or toddler, I ask that you sit close to the door so that, in case your little one needs special attention and is disrupting the learning of other students, you may step outside of class until their needs are met. Seats close to the door are reserved for parents attending class with their child.

Footnotes

  1. Shamelessly stolen/adapted from similar policy by Drs. Melissa Cheney, Guy Grossman and Rohan Alexander↩︎