Main Field of Study and progress level:
Chemistry: Second cycle, in-depth level of the course cannot be classified
Grading scale: Pass with distinction, Pass, Fail
Responsible department: Department of Chemistry
Revised by: Faculty Board of Science and Technology, 2021-08-10
Contents
The course comprizes: -regressions models for multivariate calibration, and in particular for spectroscopic data. - the philosophy behind regression models and calibration, how they are constructed and their limitations. - validation of models and visualization of results. -different types of preprocessing of spectroscopical data and transformation of data. -the importance of statistical experimetal design for the construction of calibration and validation sets. -extensions of multivariate calibration models to for example hierarchical models. -a number of important applications.
Expected learning outcomes
After the course the student should: -describe how a multivariate calibration should be performed in a scientific correct way -describe the theory behind multivariate calibration -construct calibration set by using experimental design -select suitable regression methods, modelling the data and do correct validations. -make pre-treatment of data in a correct way
Required Knowledge
Chemometrics (5KE078, 15 ECTS), or Chemometrics (5KE053, 7.5 ECTS), or the equivalent. English proficiency equivalent to IELTS Academic Training -minimum score 5.0 with no individual score below 4.5 (tests taken before January 2005 not admissible) or TOEFL - minimum score 500 on paper based test and not below 4.0 on the TWE, Alternatively 173 on computer based test with iBT61 is also required as well as basic entrance requirements for higher studies in Swedish language proficiency if the course is taught in Swedish.
Literature
The literature list is not available through the web.
Please contact the faculty.
