MVE155 / MSG200 Statistical inference

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 

See also 

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'