Design of Experiments (152084)

Students receive the necessary theoretical and practical knowledge about the design of experiments.

• Lectures
  Designed to provide necessary theoretical background.
• Practicum
  The tutorials consist of a statistical analysis of the examples using the software package R, and interpretation of the results. They are conducted in groups of 10-15 students.
• Field work
  Visit to the field trials of the Department of Plant Breeding, Genetics and Biometrics.
• Seminars
  Three "case studies" - a description, statistical analysis and interpretation of results in the form of scientific paper.

Obveze nastavnika

All teaching materials are organized and to complement appropriate teaching units. They are ready to became available through the Merlin – the system for e-learning, which includes forum for communication with students, calendar of major events, information related to the course, on-course assignments, instructions for the use of teaching materials, evaluation methods for seminars and written examinations.

Obveze studenta

Attending lectures, tutorials and seminars is required, and the students should participate in e-learning. It is expected that the students will be able to log into the Merlin system for e-learning Merlin during the first two weeks of classes, and use it to get access to lecture presentations, examples of solved problems from seminars and other materials. Prerequisite for the final exam is regular attending to lectures and tutorials, as well as the successful completion of seminars.

1. Introduction to experimentation L - Principles of experimentation; introduction to basic terms in experimentation – treatments, plots, replication, randomization.
2. Introduction to experimentation L, P - Linear models, least squares, treatment comparisons/multiple comparisons, residuals. Introduction to R – software environment for statistical computing.
3. Introduction to experimentation P, F - Model building in R. Visit to experimental field of Department of Plant Breeding, Genetics and Biometrics.
4. Multifactorial experiments L - Analysis of multifactorial experiments – treatment structure, factors, levels, additivity, interaction, fixed and random effects, hierarchical and crossclassified designs.
5. Multifactorial experiments P - Modelling in R
6. Complete block designs L - Design structure, blocks/replicates, experimental error control, design efficiency – completely randomized design, randomized complete block design
7. Complete block designs P - Modelling in R
8. Complete block designs L - Latinized designs – Latin square
9. Complete block designs S - Modelling in R – "case study"
10. Incomplete block designs L - Block size, experimental error control, treatment comparisons, recovery of inter-block information, design efficiency – alpha design, row-column design.
11. Incomplete block designs P - Modelling in R
12. Split-plot designs L - Unequal plot size, multiple errors, treatment comparisons, multifactorial designs – split-plot
13. Split-plot designs P - Modelling in R
14. Some advanced designs L - Repeated measures, analysis of covariance, «crossover» design
15. Exam

1. Kuehl, R.O., 2000. Design of Experiments. Duxbury, Pacific Grove

1. Gomez, KA, and Gomez, AA, 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons

• Experimental Design, BOKU
• Advanced Statistics course: Design of Experiments, Wageningen UR
• Experimental Trials in Agriculture, University of Hohenheim
• Linear mixed models and experimental design with applications to agricultural field experiments, Swedish University of Agricultural Sciences