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SYLLABUS
Multivariate Statistical Methods, 6 ECTS Credits
 
COURSE CATEGORY   Master´s Programme in Statistics, Data Analysis and Knowledge Discovery
MAIN FIELD OF STUDY   Statistik - STA
SUBJECT AREA  
  COURSE CODE   732A37
AIM OF THE COURSE
After completion of the course, the student should be able to:
- use multivariate inference methods generalizing widely used univariate methods
- demonstrate insightful understanding of covariance structures in the analysis of multivariate data
- select and apply suitable methods for extracting, summarizing and analyzing the information carried by multivariate data
CONTENTS
The course content comprises practical as well as theoretical elements, for example:
- computer exercises
- training in matrix algebra
- multivariate normal distribution and inference of mean vectors
- principal component analysis and factor analysis
- canonical correlation analysis
TEACHING
Self-studies of the textbook supplemented with lectures and seminars. The lectures are devoted to presentation of theories, concepts and methods. The seminars comprise student presentations and discussions of assignments.
Computer exercises, in which the students have access to supervision, provide practical experience of analyzing multivariate data. Language of instruction: English
EXAMINATION
Hand-in problems encompassing computer-based data analysis. A final oral or written examination.

Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.
ADMISSION REQUIREMENTS

Students entering the course should have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, and computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required.
Documented knowledge of English equivalent to "Engelska B"; i.e. English as native language or an internationally recognized test, e.g. TOEFL (minimum scores: Paperbased 575 + TWE-score 4.5, internetbased 90 TWE-score 20), IELTS, academic (minimum score: Overall band 6.5 and no band under 5.5), or equivalent.
GRADING
The course is graded according to the ECTS grading scale A-F
CERTIFICATE
Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.
COURSE LITERATURE
The course literature is decided upon by the department in question.
OTHER INFORMATION
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
 
Multivariate Statistical Methods
Multivariate Statistical Methods
 
Department responsible
for the course or equivalent:
IDA - Department of Computer and Information
           
Registrar No: 1330/06-41   Course Code: 732A37      
    Exam codes: see Local Computer System      
Subject/Subject Area : Statistik - STA          
           
Level   Education level     Subject Area Code   Field of Education  
A1X   Advanced level     STA   SA  
The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-10