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Vår 2017

BIO-8514 Advanced ecological statistics - 10 stp


The course is administrated by

Faculty of Biosciences, Fisheries and Economics

Course contents

By covering theory and practice, the PhD course BIO-8514 provides guidelines for correct choice, implementation, interpretation and presentation of statistical analyses in biology with particular focus on multivariate methods.

The course consists of an intensive week of theory, covered in lectures by Prof Michael Greenacre, and related practicals, in pclab sessions by Dr Raul Primicerio using the statistical software R. During the intensive week we will cover a broad range of statistical methods including clustering, Principal Component Analysis (PCA), Correspondence Analysis (CA), Constrained Correspondence Analysis (CCA). The course will also cover statistical modeling and graphic presentation (e.g. dendrograms, biplots).

Application deadline

PhD students registered to PhD program at UiT: Apply for admission by registering in StudentWeb before February 1.

Others: Apply for a seat before 1 December. Application code 9303 in SøknadsWeb. Remember to state which course you apply for.

Contact Ingjerd Gauslaa Nilsen (ingjerd.nilsen@uit.no) at the BFE-faculty if you have troubles or questions regarding registration to the course.

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+2 years (or equivalent) may be admitted. Valid documentation is a statement from your institution that you are a registered PhD student, or a Master´s Diploma with Diploma Supplement / English translation of the diploma. PhD students are exempt from semester fee.

Objective of the course

Knowledge:

Skills:

General competence:


Teaching methods

The course will be dominated by practical exercises at the PC-laboratory (40 h). Lectures and seminars in theory will be given (10 h), and there is an opportunity to develop this part based on feedback from the students. Students needs and wishes will to some extent decide course content. Special topics could be given as intensive lectures or PC-labs.

Assessment

Written course report and one or two presentations during the course.
Lectures Spring 2017