MVE155 / MSG200 Statistical inference Spring 25
MVE155 / MSG200 Statistical inference (7.5 hp) 2025
Course is offered by the department of Mathematical Sciences
Teachers
Lectures: Aila Särkkä (aila@chalmers.se)
Exercises/Lectures: Adrien Malacan (malacan@chalmers.se)
Course purpose
"Statistical Inference" is a second course in mathematical statistics suitable for students with different backgrounds. A main prerequisite is an introductory course in probability and statistics. The course gives a deeper understanding of some traditional topics in mathematical statistics such as methods based on likelihood, aspects of experimental design, non-parametric testing, analysis of variance, introduction to Bayesian inference, chi-squared tests, multiple regression.
Preliminary schedule
The notes below will be updated during the course.
|
Mon 20/1, 13:15-15:00 |
Lecture: Introduction; Parametric models (notes, Chapter 1) |
Chapter 1 |
|
Tue 21/1, 13.15-15.00 |
Lecture: Parametric models | Chapter 1 |
|
Fri 24/1, 13.15-15.00 |
Lecture/Exercises |
Chapter 1 |
| Mon 27/1, 13.15-15.00 | Lecture: Random sampling (notes, Chapter 2) | Chapter 2 |
| Tue 28/1, 13.15-15.00, KC |
Lecture: Random sampling |
Chapter 2 |
| Wed 29/1, 13.15-15.00 | Exercises | Chapter 2 |
| Fri 31/1, 13.15-15.00 |
Lecture: Parameter estimation (notes, Chapter 3) |
Chapter 3 |
| Mon 3/2, 13.15-15.00 | Exercises | Chapter 3 |
| Fri, 7/2 13.15-15.00 |
Lecture (Adrien Malacan): Hypothesis testing (notes, Chapter 4) |
Chapter 4 |
| Mon 10/2, 13.15-15.00 |
Lecture (Adrien Malacan): Hypothesis testing |
Chapter 4 |
| Tue 11/2, 13.15-15.00 | Lecture: Bayesian inference (notes, Chapter 5) | Chapter 5 |
| Wed 12/2, 13.15-15.00 | Exercises | Chapter 4 |
| Fri 14/2, 13.15-15.00 | Exercises | Chapter 5 |
| Mon 17/2, 13.15-15.00 |
Lecture: Summarising data (notes, Chapter 6) |
Chapter 6 |
| Tue 18/2, 13.15-15.00 | Lecture: Summarising data |
Chapter 6 |
| Wed 19/2, 13.15-15.00 | Exercises | Chapter 6 |
| Fri 21/2, 13.15-15.00 | Lecture: Comparing two samples (notes, Chapter 7) | Chapter 7 |
| Mon 24/2, 13.15-15.00 | Lecture: Comparing two samples | Chapter 7 |
| Tue 25/2, 13.15-15.00 | Lecture: Analysis of variance (notes, Chapter 8) | Chapter 8 |
| Wed 26/2, 13.15-15.00 | Exercises | Chapter 7 |
| Fri 28/2, 13.15-15.00 |
Lecture: Analysis of variance |
Chapter 8 |
| Mon 3/3, 13.15-15.00 | Exercises | Chapter 8 |
| Tue 4/3, 13.15-15.00 |
Lecture: Categorical data analysis (notes, Chapter 9) |
Chapter 9 |
| Wed 5/3, 13.15-15.00 |
Exercises |
Chapter 9 |
| Fri 7/3, 13.15-15.00 | Lecture (Adrien Malacan): Multiple regression (notes, Chapter 10) | Chapter 10 |
| Mon 10/3, 13.15-15.00 |
Guest lecture by Magnus Pettersson, Statistikkonsulterna Väst AB |
|
| Tue 11/3, 13.15-15.00 | Lecture (Adrien Malacan): Multiple regression | Chapter 10 |
| Wed 12/3, 13.15-15.00 | Exercises | Chapter 10 |
| Fri 14/3, 13.15-15.00 |
Questions and answers session |
Chapters 1-10 |
| Tue 18/3, 14:00-18:00 | Exam |
Course literature
The course is build around the compendium - click and download. The compendium may undergo minor updates during the course.
Recommended additional textbook: Mathematical statistics and data analysis, 3rd edition (2nd edition is also OK), by John Rice (Cremona).
Learning objectives and syllabus
Learning objectives:
- summarize multiple sample data in a meaningful and informative way,
- recognize several basic types of statistical problems corresponding to various sampling designs,
- estimate relevant parameters and perform appropriate statistical tests for multiple sample data sets.
Link to the syllabus at Chalmers
Use of AI tools
During your studies, you are free to use AI tools to support your learning. During the exam, the use of any kind of AI tools is not allowed.
Examination form
The grading of the course is based on a written examination. Preparing for the final exam, check Section 12.1 of the Compendium to see the list of the topics that may be addressed by the final exam questions.
You are allowed to use a Chalmers allowed calculator and your own course summary (four A4 pages) during the final exam. Importantly, this summary should not be produced by copying and pasting of different parts of the compendium. Tables of distribution values will be provided for you.
Several old exams with solutions are given in the module "Old exams".
Maximal number of points for the final exam is 30. Passing limits
- Chalmers students: 12 points for '3', 18 points for '4', 24 points for '5'
- GU students: 12 points for 'G', 20 points for 'VG'