Multivariate statistics
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T550001102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master
Course ID: T550001101
ECTS value: 5
Date of Approval: 12-07-2022
Duration: 1 semester
Version: Approved - active
Course ID
Course Title
ECTS value
5
Internal Course Code
Responsible study board
Administrative Unit
Date of Approval
Course Responsible
| Name | Department | |
|---|---|---|
| Kamilla Juel Sørensen | kjs@tek.sdu.dk | TEK Uddannelseskoordinering og -support |
| Preben Hagh Strunge Holm | prebenh@mmmi.sdu.dk | SDU Robotics |
Teachers
Programme Secretary
| Name | Department | City | |
|---|---|---|---|
| Susanne Bech Fogtmann | sfo@tek.sdu.dk | TEK Uddannelseskoordinering og -support |
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Basic statistics:
Basic theory of probability, probability density function, normal distribution, descriptive statistics and visualization of data sets, parameter estimation, confidence intervals, hypothesis testing, correlation and simple linear regression, analysis of variances (ANOVA)
Linear algebra:
Basic skills in manipulation of vector-matrix equations, matrix determinants, matrix inversion, eigenvalue decomposition
Recommended prerequisites
Recommended prerequisites
Basic experience with defining and manipulating vectors and matrices and analyzing data sets in MATLAB
Learning objectives - Knowledge
After the course the student can:
- Explain multivariate distributions in general and the multivariate normal distribution in particular including its sampling distributions
- Explain multivariate hypothesis testing and multivariate analysis of variance
- Explain multivariate linear regression and the General Linear Model
- Explain methods for dimension reduction or analysis of covariance structure such as Principal Components Analysis and Factor analysis
- Explain methods for detection or classification such as linear and quadratic discriminant analysis
- Explain methods for grouping and clustering of multivariate observations
- Explain the assumptions for the mentioned methods
- Explain in which situations each of the methods apply
Learning objectives - Skills
After the course the student can
- Implement the above mentioned methods on an appropriate numerical platform, such as, e.g., MATLAB and its Statistics Toolbox
- Calculate the relevant quantitative descriptive statistics for the above methods
- Make appropriate visualizing plots for each of the methods
- Do relevant inferential statistics for each of the methods
Learning objectives - Competences
After the course the student can
- plan and design statistical experiments in a multivariate setting, where the observed or measured data involve several possibly correlated response variables
- analyze the collected data using one or several appropriate multivariate methods
- perform model check to verify the assumptions for the chosen method(s) of analysis
- summarize the analysis using quantitative as well as qualitative methods
- visualize the results of the analysis
- conclude on the performed multivariate analysis
- identify and apply the multivariate statistical methods as parts of statistical algorithms within fields such as signal processing, stochastic control, computer vision, machine learning and others
URL for Skemaplan
Teaching Method
Number of lessons
48 hours per semester
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Examination is held
By the end of the semester
Tests
Exam
EKA
T550001102
Name
Exam
Form of examination
Oral examination
Censorship
Second examiner: Internal
Grading
7-point grading scale
Identification
Student Identification Card - Date of birth
Language
English
ECTS value
5
Additional exam information
The form of examination in the re-examination is the same as in the ordinary examination.
Exam regulations
Name
Exam regulations
Courses offered
| Offer period | Offer type | Profile | Education | Semester |
|---|---|---|---|---|
| Fall 2023 | Optional | Kandidat i energisystemer, optag 2022, energiinformatik | Master of Science in Engineering (Energy Systems) | Master of Science in Engineering (Energy Systems) | Odense | |
| Fall 2023 | Optional | Kandidat i energisystemer, optag 2022, energisystemer | Master of Science in Engineering (Energy Systems) | Master of Science in Engineering (Energy Systems) | Odense | |
| Fall 2023 | Mandatory | MSc in Robot Systems, 2022, Drones and Autonomous Systems (DAS) | Master of Science in Engineering (Robot Systems) | Odense | 1 |
| Fall 2023 | Mandatory | MSc in Robot Systems, 2022, Advanced Robotics Technology (ART) | Master of Science in Engineering (Robot Systems) | Odense | 1 |
| Fall 2023 | Mandatory | MSc in Robot Systems, 2023, Advanced Robotics Technology (ART) | Master of Science in Engineering (Robot Systems) | Odense | 1 |
| Fall 2023 | Mandatory | MSc in Robot Systems, 2023, Drones and Autonomous Systems (DAS) | Master of Science in Engineering (Robot Systems) | Odense | 1 |
| Fall 2023 | Optional | MSc in Physics and Technology, 2023 | Master of Science in Engineering (Physics and Technology) | Odense | |
| Fall 2023 | Optional | MSc in Physics and Technology, 2022 | Master of Science in Engineering (Physics and Technology) | Odense |