Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMSF65F valid from Autumn 2020

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  • 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.
  • 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.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to explain and use basic methods in factorial experiments,
    be able to explain and use basic methods in analysis of variance with fixed and random effects, regression and analysis of covariance.
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to plan a factorial experiment,
    be able to suggest an experimental plan suitable for a given problem,
    be able to structure and analyse sets of data using a computer package and critically examine the result,
    be able to, both in written reports and orally at seminars, account for the solutions of statistical problems
Judgement and Approach
  • For a passing grade the doctoral student must
Types of Instruction
  • Lectures
  • Laboratory exercises
  • Exercises
  • Project
Examination Formats
  • Written exam
  • Written report
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Basic mathematical statistics knowledge and programming experience.
Selection Criteria
  • Box, George E. P., Hunter, J. Stuart & Hunter, W.: Statistics for experimenters: design, innovation, and discovery. Wiley-Blackwell, 2005. ISBN 9780471718130.
    Montgomery, Douglas C.: Design and Analysis of Experiments. 2019. ISBN 9781119492443.
Further Information
Course code
  • FMSF65F
Administrative Information
  • 2020-08-26
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2021‑03‑21 2021‑06‑06 2021‑03‑08

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