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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
KBT120 - Design and analysis of experiments  
 
Syllabus adopted 2014-02-13 by Head of Programme (or corresponding)
Owner: MPISC
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering
Department: 21 - CHEMISTRY AND CHEMICAL ENGINEERING


Teaching language: English
Open for exchange students
Block schedule: D

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5 c Grading: TH   7,5 c   31 Oct 2014 am V,  05 Jan 2015 am V,  20 Aug 2015 am M  

In programs

MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 2 (elective)
TKBIO BIOENGINEERING, Year 3 (compulsory)

Examiner:

Professor  Claes Niklasson


Course evaluation:

http://document.chalmers.se/doc/868cd7bf-6964-4afc-aba4-13d7c1c7be43


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

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

Written examination (5 hours)


Page manager Published: Thu 04 Feb 2021.