Course syllabus adopted 2021-02-16 by Head of Programme (or corresponding).
Overview
- Swedish namePlanering och utvärdering av experiment
- CodeKBT120
- Credits7.5 Credits
- OwnerMPISC
- Education cycleSecond-cycle
- Main field of studyBioengineering, Chemical Engineering
- DepartmentCHEMISTRY AND CHEMICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 25115
- Maximum participants120
- Minimum participants12
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0107 Examination 7.5 c Grading: TH | 7.5 c |
|
In programmes
- MPAUT - AUTOMOTIVE ENGINEERING, MSC PROGR, Year 2 (elective)
- MPISC - INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 1 (compulsory)
- MPMCN - MATERIALS CHEMISTRY, MSC PROGR, Year 1 (compulsory elective)
- MPMCN - MATERIALS CHEMISTRY, MSC PROGR, Year 2 (elective)
- MPNAT - NANOTECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)
- MPNAT - NANOTECHNOLOGY, MSC PROGR, Year 2 (elective)
- TKBIO - BIOENGINEERING, Year 3 (compulsory)
Examiner
Claes Niklasson- Full Professor, Chemical Engineering, Chemistry and Chemical Engineering
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 ExperimentsExamination including compulsory elements
Written examination (5 hours)The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.