Course syllabus
MVE155 / MSG200 Statistical inference (7.5 hp) 2023
Course is offered by the department of Mathematical Sciences
Teacher
Lectures: Aila Särkkä
Exercises: Tony Johansson
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.
The exam questions and solutions (March 14, 2023)
The exam questions and solutions (August 15, 2023)
Preliminary schedule
|
Mon 16/01, 13:15-15:00, Vasa B |
Lecture: Introduction; Parametric models (notes, Chapter 1) |
Chapter 1 |
|
Tue 17/01, 13.15-15.00, KC |
Lecture: Parametric models | Chapter 1 |
|
Wed 18/01, 13.15-15.00, HC3 |
Exercises | Chapter 1 |
|
Fri 20/01, 13.15-15.00, KA |
Lecture: Random sampling (notes, Chapter 2) |
Chapter 2 |
| Mon 23/01, 13.15-15.00, KC | Exercises | Chapter 2 |
| Tue 24/01, 13.15-15.00, KC |
Lecture: Random sampling |
Chapter 2 |
| Wed 25/01, 13.15-15.00, KA | Exercises (R code: Problem 7, Problem 10) | Chapter 2 |
| Fri 27/01, 13.15-15.00, KA | Parameter estimation (notes, Chapter 3) | Chapter 3 |
| Mon 30/01, 13.15-15.00, KA | Exercises | Chapter 3 |
| Tue 31/02, 13.15-15.00, KC |
Lecture: Hypothesis testing (notes, Chapter 4) |
Chapter 4 |
| Wed 01/02, 13.15-15.00, KA | Exercises | Chapter 4 |
| Fri 03/02, 13.15-15.00, KA | Lecture: Hypothesis testing | Chapter 4 |
| Mon 06/02, 13.15-15.00, Pascal | Exercises | Chapter 4 |
| Fri 10/02, 13.15-15.00, KB | Lecture: Bayesian inference (notes, Chapter 5) | Chapter 5 |
| Mon 13/02, 13.15-15.00, Pascal |
Exercises |
Chapter 5 |
| (notes, Chapter 7)Tue 14/02, 13.15-15.00, Pascal | Lecture: Summarising data (notes, Chapter 6) |
Chapter 6 |
| Wed 15/02, 13.15-15.00, Pascal | Exercises (R code) | Chapter 6 |
| Fri 17/02, 13.15-15.00, KA | Lecture: Summarising data, Comparing two samples (notes, Chapter 7) | Chapter 7 |
| Mon 20/02, 13.15-15.00, Pascal | Exercises | Chapter 7 |
| Tue 21/02, 13.15-15.00, Pascal | Lecture: Comparing two samples, Analysis of variance (notes, Chapter 8) | Chapter 8 |
| Wed 22/02, 1(notes, Chapter 8)3.15-15.00, Pascal | Exercises | Chapter 8 |
| Fri 24/02, 13.15-15.00, KA |
Lecture: Analysis of variance |
Chapter 8 |
| Mon 27/02, 13.15-15.00, Pascal | Exercises | Chapter 8 |
| Tue 28/03, 13.15-15.00, Pascal |
Lecture: Categorical data analysis (notes, Chapter 9) |
Chapter 9 |
| Wed 01/03, 13.15-15.00, Pascal |
Exercises |
Chapter 9 |
| Fri 03/03, 13.15-15.00, KA | Lecture: Multiple regression (notes, Chapter 10) | Chapter 9 |
| Mon 06/03, 13.15-15.00, Pascal |
Exercises |
Chapter 9 |
| Tue 07/03, 13.15-15.00, Pascal | Lecture: Multiple regression | Chapter 10 |
| Wed 08/03, 13.15-15.00, Pascal | Exercises | Chapter 10 |
| Fri 10/03, 13.15-15.00, KA |
Questions and answers session |
Chapters 1-10 |
| Tue 14/3, 14:00-18:00 | Exam (register before 26.02.2023) |
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 on Studieportalen: Study plan
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.
Several old exams with solutions are given in the module "Old exams". Aila's old exams of the course Experimental design: Exam 1 and solutions, Exam 2.
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'