Statistical inference

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MVE155 / MSG200 Statistical inference lp3 VT20 (7.5 hp)

Course is offered by the department of Mathematical Sciences

Teachers

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.

Schedule

Date and place Time plan according to the Lecture notes

Mon 20/01, 13.15-15.00, KB

 Lecture 1: Chapters 1-3

Tue 21/01, 13.15-15.00, KC

 Exercise 1: 3.6.1, 3.6.2, 3.6.6

Wed 22/01, 13.15-15.00, HC4

 Lecture 2: Chapters 3-4

Mon 27/01, 13.15-15.00, HA2

 Exercise 2: 3.6.7, 3.6.8, 4.7.1, 4.7.4
Tue 28/01, 13.15-15.00, KC Lecture 3: Chapter 4
Wed 29/01, 13.15-15.00, KC Exercise 3:  4.7.3, 4.7.5, 4.7.6, 4.7.7
Fri 31/01, 13.15-15.00, KA Lecture 4: Chapter 5
Mon 03/02, 13.15-15.00, Pascal Exercise 4: 5.8.1, 5.8.4, 5.8.6, 5.8.7, 5.8.10
Fri 07/02, 13.15-15.00, KA Lecture 5: Chapter 5-6
Mon 10/02, 13.15-15.00, Pascal Exercise 5: 5.8.2, 5.8.9, 5.8.12, 6.5.1, 6.5.4
Tue 11/02, 13.15-15.00, Pascal Lecture 6: Chapter 6
Wed 12/02, 13.15-15.00, Pascal Exercise 6: 6.5.2, 6.5.3, 6.5.5, 6.5.6
Fri 14/02, 13.15-15.00, KA Lecture 7: Chapter 7
Mon 17/02, 13.15-15.00, Pascal Exercise 7: 7.7.1, 7.7.4, 7.7.5, 7.7.6, 7.7.8
Tue 18/02, 13.15-15.00, Pascal Lecture 8: Chapter 8
Wed 19/02, 13.15-15.00, Pascal Exercise 8: 8.6.2, 8.6.3, 8.6.4, 8.6.6
Mon 24/02, 13.15-15.00, Pascal Lecture 9: Chapter 8-9
Tue 25/02, 13.15-15.00, Pascal Exercise 9: 8.6.7, 8.6.8, 8.6.11, 9.8.1, 9.8.2
Wed 26/02, 13.15-15.00, Pascal Lecture 10: Chapter 9
Fri 28/02, 13.15-15.00, KA Exercise 10: 9.8.3, 9.8.4, 9.8.5, 9.8.6
Mon 02/03, 13.15-15.00, Pascal Lecture 11: Chapter 9-10
Tue 03/03, 13.15-15.00, Pascal Exercise 11: 9.8.7, 10.5.2, 10.5.3, 10.5.4
Wed 04/03, 13.15-15.00, Pascal Lecture 12: Chapter 10
Fri 06/03, 13.15-15.00, KA Exercise 12: 10.5.6, 10.5.7, 10.5.9, list of course topics
Mon 09/03, 13.15-15.00, Pascal Lecture 13: Chapter 11
Tue 10/03, 13.15-15.00, Pascal Exercise 13: 11.6.2, 11.6.4, 11.6.5, 11.6.7
Wed 11/03, 13.15-15.00, Pascal Lecture 14: Chapter 11
Fri 13/03, 13.15-15.00, KA Exercise 14: 11.6.3, 14.1.1, 14.1.14, 14.1.21, 14.1.9
Tue 17/03, 14.00-18.00 Exam 1 (register before 1/03)
Tue 09/06, 8.30-12.30 Exam 2 (register before 24/05)
Thu 27/08, 14.00-18.00 Exam 3 (register before 02/08)

Course literature

Recommended textbook: Mathematical statistics and data analysis, 3rd edition, by John Rice (Cremona)

 Lecture notes - click and download. These lecture notes undergo minor updates - on the first page you will see when the notes were last updated.

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. The final exam is closed books. However, you will be allowed to use four A4 pages of notes. These notes should be your own summary of the course material and might be either hand-written or printed out from your own file. You are allowed to use a calculator with free memory and no possibility for internet connection. Several old exams with solutions are given in the module "Old exams".

Maximal number of points for the final exam is 30 (plus max 3 bonus points for the optional assignment). Passing limits 

  • CTH students: 12 points for '3', 18 points for '4', 24 points for '5'
  • GU students: 12 points for 'G', 20 points for 'VG'

When grading exams, the following signs are used

  • underline - a place where it starts getting wrong or a wrong answer
  • three dots - the answer is not complete
  • question sign - I do not understand here or disagree
  • plus - you made a good point here

Course summary:

Date Details Due