Syllabus academic year 2011/2012
(Created 2011-09-01.)
DESIGN OF EXPERIMENTSFMS072
Credits: 7,5. Grading scale: TH. Cycle: G2 (First Cycle). Main field: Technology. Language of instruction: The course might be given in English. FMS072 overlaps following cours/es: MAS209 and MASC05. Alternative for: W3. Optional for: D4, E4, E4ssr, F4, F4bm, MWIR2, N4, Pi4, Pi4bm, Pi4mrk. Course coordinator: Anna Lindgren, Director of studies, studierektor@matstat.lu.se, Mathematical Statistics. Recommended prerequisits: Basic mathematical statistics and Matlab. The course might be cancelled if the number of applicants is less than 16. Assessment: Written reports as well as compulsory and active participation in the seminars. Further information: The course is also given at the Faculty of Science with the code MASC05. Home page: http://www.maths.lth.se/matstat/kurser/fms072/.

Aim
This is a basic course in designing experiments and analyzing the resulting data. It is intended for engineers, physical/chemical scientists and scientists from other fields such as biotechnology and biology. The course deals with the types of experiments that are frequently conducted in industrial settings. Its objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all phases of engineering and scientific work, including technology development, new product design and development, process development, and manufacturing process improvement. Applications from various fields of engineering (including chemical, mechanical, electrical, materials science, industrial, etc.) will be illustrated throughout the course.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

Contents
Simple design with fixed and random effects. Simultaneous confidence intervals. Requirements for analysis of variance: transformations, model validation, residual analysis. Factorial design with fixed, random, and mixed effects. Additivity and interaction. Complete and incomplete designs. Randomised block designs, Latin squares and confounding. Regression and analysis of covariance.

Literature
Montgomery, D.C: Design and Analysis of Experiments, 5th edition. Wiley 2001.