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Syllabus for

Academic year
KBT120 - Design and analysis of experiments
Planering och utvärdering av experiment
 
Syllabus adopted 2020-02-20 by Head of Programme (or corresponding)
Owner: MPISC
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering
Department: 21 - CHEMISTRY AND CHEMICAL ENGINEERING

 
Teaching language: English
Application code: 25118
Open for exchange students: Yes
Block schedule: D+
Maximum participants: 120

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5c Grading: TH   7,5c   30 Oct 2020 am J   05 Jan 2021 am J,  19 Aug 2021 am J

In programs

TKBIO BIOENGINEERING, Year 3 (compulsory)
MPAUT AUTOMOTIVE ENGINEERING, MSC PROGR, Year 2 (elective)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPNAT NANOTECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)
MPNAT NANOTECHNOLOGY, MSC PROGR, Year 2 (compulsory elective)
MPMCN MATERIALS CHEMISTRY, MSC PROGR, Year 2 (compulsory elective)
MPMCN MATERIALS CHEMISTRY, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Claes Niklasson

  Go to Course Homepage


Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Fundamental statistics

Aim

The aim of the course to give competences in the field of applied statistical methods for work concerning planning and analysis of experiments, regression analysis, optimization of processes and multivariate analysis.

Learning outcomes (after completion of the course the student should be able to)

  • Plan experiments according to a proper and correct design plan.
  • Analyse and evaluate experimental results (statistically), according to chosen experimental design (ANOVA, regression models).
  • Control and properly use fundamentals such as hypothesis testing, degrees of freedom, ANOVA, fractional design and other design methods/techniques and so on.
  • Know the fundamentals of multivariate analysis and chemometric methods (PCA and PLS) with simple applications.

Content

  • Statistics
  • Simple Comparative Experiments
  • Experiments of a single factor, analysis of variance.
  • Randomized blocks
  • Latin squares
  • The 2k factor design
  • Blocking and confounding
  • Two level fractional Factorial design.
  • Three level and mixed level factorial and fractional factorial design.
  • Fitting regression methods. LS method.
  • Robust parameter design
  • Experiment with random factors.
  • Nested design
  • Response surfaces, EVOP.
  • Multivariate data analysis

Organisation

The course contains lectures mixed with calculation examples showing practical applications of basic theories. The assignments and calculation are based on realistic industrial examples taken from literature and research projects. The projects are problem based with active learning activities. This part has been a very successful part in terms of life long learning for the students and highly appreciated among students for many years.

Literature

Douglas C. Montgomery: Design and Analysis of Experiments

Examination including compulsory elements

Written examination (5 hours)


Published: Mon 28 Nov 2016.