Learning outcome

On successful completion of the module, students will have learning outcomes in the form of acquired knowledge, skills, and general competence, as described below.

Knowledge

On successful completion of the module the students will be able to:
understand multivariate analysis and calibration techniques
understand basic experimental design approaches
understand the theory and practice of sampling

Skills

On successful completion of the module, the candidate will be able to:
use experimental design approaches
analyse multivariate data
calibrate multivariate models for prediction
apply theory of sampling in practice

General competence

On successful completion of the module, the candidate will be able to:
communicate acquired knowledge in specific subtopics involving concrete practical assignments in the form of technical reports and oral presentations to peers and staff.
[1] Codes given in parentheses correspond to the codes given in the programme description.

Course Description

Multivariate Data Analysis: Experimental design, significant testing, factorial design, fractional factorial design, regression, MLR, PCA (principal component analysis) multivariate calibration: PCR (principal component regression), PLS-R (partial least squares regression), validation of multivariate prediction models, overview of multivariate data analysis methods, applications/ examples and computer assignments.

Assessment Methods

A set of mandatory assignments count 40% and an individual written final test counts 60% of the final grade.
No study aids are permitted during the final written exam.
The assignments and the final test are used to assess knowledge and skills. The assignments are also used to assess general competence.

Minor adjustments may occur during the academic year, subject to the decision of the Dean