Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
6.11.2019 at 08:00 - 22.11.2019 at 16:00

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 25.11.2019
08:30 - 17:00
Tue 26.11.2019
08:30 - 13:00
Tue 26.11.2019
13:00 - 17:00
Wed 27.11.2019
08:30 - 17:00
Thu 28.11.2019
08:30 - 13:00
Thu 28.11.2019
13:00 - 17:00
Fri 29.11.2019
08:30 - 17:00

Description

The student will get the tools to make/select a good design of experiments, proper sampling and the optimal data analysis resulting in stronger conclusions drawn out of the data obtained.

This course gives an introduction to design of experiments and grounds how to select an applicable design of experiments to different cases. Such cases include different kind of variables (both quantitative and qualitative) with various levels and limitations due to sampling and adjustment of variable levels (hard-to-change and easy-to-change variables/parameters) etc. Also, a proper sampling and optimal data analysis are covered. It is also discussed what to do if there are missing values or missing variable in the data. The software used in the course will be MODDE.

This is an intense five-day course giving the participants an introduction into the following aspects:

  • Design of experiments: What is design of experiments? Which are main differences between different designs of experiments?
  • Criteria in selection of design of experiments: How do you select a good design of experiments to your study? What do you have to take into account? What limitations do your study have? How do you ensure your ability to answer the questions/hypothesis you would like to answer/confirm?
  • Sampling: Once you have decided on the design it is imperative to also consider how to present your sample to the measurement equipment you are using. How do you do proper sampling? How can you evaluate your sampling errors? What limitations do you have with regards to the sampling?
  • Data analysis: How should the data be analyzed? What possibilities exist, and how to do it in practice? How to deal with dummy variables? How many samples and/ or replicates do you need in order to investigate a difference between two treatments/ parameter settings?

The subject the course focuses on: Food Technology, Food Chemistry, Nutrition, Biosciences

3 ECTS including course participation and a report basing on the data analysis of the student’s own data.

Course organizer: Docent Kirsi Jouppila, Department of Food and Nutrition, University of Helsinki; email: kirsi.jouppila@helsinki.fi

Lecturer: Associate Professor Åsmund Rinnan, University of Copenhagen, Denmark