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732A97 Multivariate Statistical Methods

Course information

Course description

This is a traditional course in multivariate statistical methods, covering the multivariate normal distribution with inference of mean vectors, principal component analysis, factor analysis and canonical correlation analysis. The course requires good knowledge of matrix algebra and undergraduate courses in statistical inference and linear statistical models.

The course is intended for students in the Masters program Statistics and Data Mining as a profile course in Statistics.



Course literature

- Course textbook: Applied Multivariate Statistical Analysis by R.A. Johnson and D.W. Wichern, Pearson New International Edition/Sixth Edition, ISBN 9781292024943.
- Auxillary book: An Introduction to Applied Multivariate Analysis with R by B. Everitt and T. Hothorn, First Edition, ISBN 978-1-4419-9649-7
- Little Book of R for Multivariate Analysis




Exam and bonus point system (possibly subject to change within November depending how the seminars work out)

The examination consists of a written exam with max score 20 points and grade limits:
A : 19p, B: 17p, C: 14p, D: 12p, E: 10p.
You are allowed to bring a pocket calculator to the exam. A descision on other aids will be made later.
Active participation in the seminars gives maximum 2 bonus points to the exam. A student who earns the bonus points will add 2 points to the exam result in order to reach grade E, D or C, 1 point in order to reach grade B, but no points in order to reach grade A. Required exam results for a student who earned the bonus points for respective grade:
A: 19p, B: 16p, C: 12p, D: 10p, E: 8p.
Active participation means that a student comes prepared to the seminar session with all the given day's exercises, correctly solves an exercise on the board, is able to answer questions about the presented solution and is able to give help and comments to the classmates' presented solutions


Schedule (preliminary check with Timeedit)

Tuesday November 7 13-15 in G32

Content: Introduction, visualization, distances. (Chapter 1), Exercise 1.17

Monday November 13 10-12 in G34

Type: Lecture
Content: Random vectors, sample geometry (Chapter 2 Int. Ed., Chapter 3 Ed. 6)

Tuesday November 14 13-15 in Planck

Type: Lecture
Content: Matrix algebra (Chapter 3 Int. Ed., Chapter 2 Ed. 6)

Monday November 20 10-12 in A37

Type: Lecture
Content: Multivariate normal distributon (Chapter 4)

Tuesday November 21 13-15 in S26

Type: Seminar
Content: Exercises (Int. Ed.) 2.5, 2.6, 2.8, 2.9, 3.26, 3.27, 3.35; (Ed. 6) 3.5, 3.6, 3.8, 3.9, 2.26, 2.27, 2.35

Thursday November 23 8-10 in A37

Type: Seminar
Content: Exercises (Int. Ed.) 3.2, 3.7, 4.2-4.5, 4.21, 4.22; (Ed. 6) 2.2, 2.7, 4.2-4.5, 4.21, 4.22

November 24 23:59 Deadline for Computer Assignment 1

Instruction and data in LISAM. Submission through LISAM.

Monday November 27 10-12 in A37

Type: Lecture
Content: Inference about a mean vector (Chapters 5,6)

Wednesday December 4 10-12 in A37

Type: Lecture
Content: Principal components analysis, Factor analysis (Chapter 8,9)

Wednesday December 5 13-15 in A37

Type: Seminar
Content: Exercises 5.1, 5.3, 5.4a, 5.7, 6.5, 6.8, 6.19, 6.22

December 8 23:59 Deadline for Computer Assignment 2

Instruction and data in LISAM. Submission through LISAM.

Wednesday December 11 10-12 in R41

Type: Lecture
Content: Canonical correlation analysis (Chapter 10)

Tuesday December 12 13-15 in R41

Type: Seminar
Content: Exercises 8.4, 8.6, 8.10, 9.10, 9.11, 9.19, 10.2, 10.12

Thursday December 14 8-10 in R41

Type: Lecture
Content: Multidimensional scaling (Chapter 12.6, Chapter 4 in Everitt, Hothorn), Exercise: 4.1 (Everitt, Hothorn)

December 15 23:59 Deadline for Computer Assignment 3

Instruction and data in LISAM. Submission through LISAM.

Monday December 18 10-12 in R41

Type: Seminar
Content: Recap, exam preparation, reserve time

December 19 23:59 Deadline for Computer Assignment 4

Instruction and data in LISAM. Submission through LISAM.

Tuesday January 9 8-12

Content: Written examination

February 4 23:59

Content: Final deadline for all assignments.
After this date no submissions nor corrections will be considered and you will have to redo the missing labs next year.


Page responsible: Krzysztof Bartoszek
Last updated: 2017-12-11