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Syllabus for

Academic year
TMS031 - Experimental design  
 
Syllabus adopted 2014-02-17 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5 c Grading: TH   7,5 c   16 Mar 2016 am M   07 Jun 2016 am M,  Contact examiner

In programs

MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)

Examiner:

Univ lektor  Kerstin Wiklander



  Go to Course Homepage

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

Basic courses in linear algebra, calculus and mathematical statistics.

Aim

This course provides the students with knowledge of working with statistics in practice, including studying data with associated errors.

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

- judge which assumptions are needed in order to draw conclusions from data using statistical methods
- apply these methods to analyse data
- know how the experiments should be planned in order to fulfil these assumptions
- plan and analyse experiments in certain typical situations
- appreciate and explain importance of experimental planning.

Content

The basic philosophy behind statistical inference is discussed. Comparative studies with two treatments, where the nuisance factors can either be randomised or blocked, will be studied. The practical implementation plays an important role for the analysis. Furthermore, more than two treatments will be introduced and treated using
analysis of variance. We also study regression analysis. When studying the analysis methods, the assumptions needed are emphasized, since they are essential during the planning stage of the experiment. The key concepts and techniques are:

 - independence and dependence, correlation 

- randomisation and blocking 

- tests and confidence intervals in comparing two treatments 

- one-way analysis of variance with and without blocking 

- two-way analysis of variance 

- regression analysis 

- factorial designs at two levels 

- fractional factorial designs 

- response surface methods.

Organisation

The course consists of lectures, exercises and projects.

Literature

To be decided

Examination

Evaluation of the projects and a written examination.


Page manager Published: Mon 28 Nov 2016.