Content, progress and pedagogy of the module

Learning objectives


Students who have passed the module should be able to

  • Account for general methods for multivariate data analysis (principal component analysis, multiple linear regression, principal component regression, projection on latent structures, soft independent modelling of class analogy)
  • Account for methods for data preprocessing (centering, scaling, non-linear and spectroscopic preprocessing, orthogonal signal correction)
  • Explain basic methods for variable selection (Selectivity ratio, VIP, interval PLS, jack-knife)
  • Explain the theoretical background of these methods, their advantages and limitations as well as possible applications
  • Explain how multivariate methods complement traditional statistical methods


  • Explore multivariate data, find groups and trends, detect and remove outliers
  • Calibrate and do proper validation of multivariate regression models, use these models for prediction
  • Evaluate if data need a preprocessing and which method to apply
  • Calibrate and evaluate models for data classification
  • Compare different regression and classification models and find which is the best
  • Use multivariate methods for analysis of real data from different applications

Type of instruction

  • Lectures, classroom instruction and mini-projects

Extent and expected workload

150 hours



Name of examChemometrics
Type of exam
Written exam
AssessmentPassed/Not Passed
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleKemometri
Module codeK-KT-K1-9
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Esbjerg
Responsible for the module


Study BoardStudy Board of Biotechnology, Chemistry and Environmental Engineering
DepartmentDepartment of Chemistry and Bioscience
FacultyFaculty of Engineering and Science