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Research methodology in forest sciencesLaajuus (3.5 cr)

Course unit code: C-10088-LM00CO21

General information


Credits
3.5 cr
Teaching language
English
Institution
University of Eastern Finland

Objective

Upon a successful completion of this course, the students are expected to be able to understand the principles of research methodology in forestry, taking into account the research issues and objective formulation as well as choosing an appropriate research approach, experimental set-up, and sampling technique. The students will be able to understand basic biometric and ecosystem modeling concepts, and to apply basic commands of R statistics to model and analyse the collected data. In addition, the students will be able to critically evaluate accuracy, error types, and reproducibility of research results. Finally, they will understand the basic concepts in Geographic Information Systems and remote sensing techniques, will use basic GIS software to solve spatial problems, and will develop their potential for forestry-related research. The course develops the following generic skills: critical thinking, digitalization, interaction and communication, identification and development of expertise, ethics, leadership and development.

Content

Applied statistics, research methodology, biometric and ecosystem modeling concepts, R statistics, GIS and remote sensing techniques.

Qualifications

It is recommended to take this course together with LM00CO20 Academic skills in forest sciences.

Materials

Hamilton, L.C. (1992) Regression with Graphics, A second course in applied statistics. Duxbury Press. Wonnacott, R. & Wonnacott, T., (1985) Introductory Statistics, 4th edition, John Wiley and Sons. Course notes and selected online materials: https://sites.uef.fi/biopro

Further information

The Course is organised annually, in the autumn semesters and the language of instruction for this course is English. The course is intended only for MSc degree students.

Execution methods

Lectures on research, data analysis, statistical concepts and methods (28 h), practical exercises with R statistics and GIS software (14 h). Modeling group work and learning diary.

Accomplishment methods

0-5 Assignments (30%) and final examination (70%)

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