Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMS210F valid from Autumn 2021

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  • Build on the knowledge in design of experiments in order to be able to plan and perform more complicated experiments, as well as analyse data in several dimensions.
  • Complete and reduced factorial designs. Response surface analysis. Cluster analysis, discriminant analysis, principal component analysis (PCA), and partial least squares (PLS).
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to explain and use basic methods in factorial design
    be able to explain and use basic methods in cluster analysis, discriminant analysis, principal components, and partial least squares
    be able to evaluate and discuss results obtained through use of multivariate statistical methods
Competences and Skills
  • For a passing grade the doctoral student must
  • plan a factorial design experiment
    suggest which multivariate statistical method should be used on a given problem
    structure and analyse multi-dimensional data materials using computer software for multivariate methods, and critically assess the result
    report the solutions of multivariate statistical problems in written reports and orally at seminars
    autonomously be able to analyse a specific problem at an advanced level with multivariate statistical methods for experimental design and multivariate data analysis and to be able to discuss these.
Judgement and Approach
  • For a passing grade the doctoral student must
Types of Instruction
  • Lectures
  • Seminars
  • Laboratory exercises
  • Exercises
  • Self-study literature review
Examination Formats
  • Written report
  • Written assignments
  • Seminars given by participants
  • The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
  • Failed, pass
Admission Requirements
  • FMA420 Linear Algebra or FMA421 Linear Algebra with Scientific Computation or FMA656 Mathematics, Linear Algebra or FMAA20 Linear Algebra with Introduction to Computer Tools or FMAB20 Linear Algebra
Assumed Prior Knowledge
  • A basic course in mathematical statistics and basic MATLAB programming.
Selection Criteria
  • Brereton, R.: Data driven extraction for science.. Wiley, 2018. ISBN 9781118904664.
Further Information
Course code
  • FMS210F
Administrative Information
  • 2021-04-14
  • Margareta Sandahl

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