English

Once every semester

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).

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

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.

Lectures

Seminars

Laboratory exercises

Exercises

Self-study literature review

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

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

A basic course in mathematical statistics and basic MATLAB programming.

Brereton, R.: Data driven extraction for science.. Wiley, 2018. ISBN 9781118904664.

FMS210F

2021-04-14

Margareta Sandahl