ST514: Multivariate Statistical Analysis
Comment
The course cannot be chosen by students who have passed ST811.
Entry requirements
Academic preconditions
Students taking the course are expected to:
- Have skills in
statistics corresponding to ST520 Applied statistics or ST521
Mathematical statistics, and calculus corresponding to one of the
calculus courses on the first year on the study programmes at the
Faculty of Science, SDU. - Be able to use the statistical software R.
- A full first year in Science or an equivalent study programme.
Course introduction
with data sets with several variables, which is important in regard to
performing statistical analyses in a broad range of research areas, such
as biology and epidemiology.
The course builds on the knowledge
acquired in the courses such as ST520 Applied statistics or ST521
Mathematical statistics, and the calculus course in the respective study
programme, and gives an academic basis for studying topics such as
biometry, that are part of the degree, as well as bachelor or master
projects where multivariate methods are employed.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- Give the competence to evaluate and choose between different methods for the analysis of multivariate data sets.
- Give skills to perform analyses of multivariate data sets using the statistical software R.
- Give knowledge and understanding of the fundamental ideas and methods for analyzing measurements on several variables.
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Reproduce key theoretical results concerning elementary operations on random variables and random vectors.
- Work with the concepts and models, both in scalar and matrix/vector representation.
- Understand and identify problems that can be solved using multivariate techniques.
- Perform a practical data analysis with the techniques from the course.
- Perform programming relevant to the content of the course in the statistical package used in the course.
- Identify and interpret relevant information in the output of the statistical package used in the course.
- Summarize the results of an analysis in a statistical report.
Content
- random vectors
- the multivariate normal distribution
- inference about a mean vector
- comparison of several mean vectors
- principal component analysis
- discriminant analysis and classification
Literature
Examination regulations
Exam element a)
Timing
Tests
Project and written exam
EKA
Assessment
Grading
Identification
Language
Examination aids
To be announced during the course
ECTS value
Additional information
The examination is based on written project reports handed in during the course and a written exam.
The examination form for re-examination may be different from the exam form at the regular exam.
Indicative number of lessons
Teaching Method
In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours aor the study phase hours.
Educational activities
- Work on problems not covered in the training phase.
- Discussion of the concepts and terms of the topic.