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KARAKTERFORDELING · V 2025
19 kandidater
Kilde: Data hentet fra Felles studentsystem ved NMBU, slik den var publisert for høsten 2026. NMBU har ikke kvalitetssikret og er ikke ansvarlig for innholdet på denne siden.
NMBU sin egen emnebeskrivelse — læringsutbytte, innhold og vurderingsform.
Mixed-effect modelling is important for modelling based on data with various kinds of grouped structures. Examples include data from clustered sample plots in forest inventories, longitudinal data where observations have been made on the same objects on repeated occasions, and hierarchical data structures where, e.g., trees on plots are the study objects. Similar data structures are also common in other disciplines than forest inventory. The effect of groups can be modelled either fixed or random. The core of expected learning outcomes is which effect to choose in the modelling approach. This course provides an introduction to the mixed-effect modelling theory starting with a theory on modelling with categorical variables through generalized linear mixed-effect modelling when non-gaussian assumptions are employed. The course gives examples of practical usage and intuition on to how the modelling methods are applied.
Upon successful completion of the course, participants will be able to understand general concepts of regression analysis with grouped data. The course is intended for students and researchers in ecology, natural resources, forestry, agriculture and environmental sciences.
The course will include the pre-course self-study through literature reading, and the post-course home exam.
The expected number of participants in the course is 25 students.
The language is English.
Knowledge and understanding
Skills and abilities
4. regression analysis with grouped data.
Before the course begins, students are expected to spend time on self-study amounting to at least 10 hours through reading Chapters 4 and 5 in "Biometry for forestry and environmental data: With examples in R" by Lauri Mehtätalo,L. Juha Lappi.
It is recommended that participants have a basic knowledge in regression analysis and statistics, e.g. acquired through participation in a basic course at the Master level.
K3 - Full bruk av KI.
Bruk av KI er tillatt, men må være i tråd med retningslinjene for bruk av kunstig intelligens (KI) ved NMBU.
The course will consist of lectures mixed with exercises. At the end of the course, students will be given the home exam tasks, which should be seen as a repetition of the previously given material.
The course will based on R statistical software.
Svetlana Saarela
Hans Ole Ørka
This course is a joint Nordic NOVA PhD course organised by Hans Ole Ørka, NMBU. Please see the course information webpage on NOVA's website for more information on the course.
The computer exercises will be done in the lecture room using private laptop computers.
9 - 13 May, 2022. 08.30 - 16.00 each day.
Kilde: Data hentet fra Felles studentsystem ved NMBU, slik den var publisert for høsten 2026. NMBU har ikke kvalitetssikret og er ikke ansvarlig for innholdet på denne siden.
Hva burde andre vite før de tar dette? Anonymt, ingen innlogging.
Basert på hvor kandidatene til eksamen var registrert (DBH).
Emner som tas i de samme studieprogrammene som NOVA-413.
| Semester | Kandidater | Stryk |
|---|---|---|
| Vår 2025 | 19 | 0 % |
| Vår 2022 | 19 | 0 % |