5 ECTS credits
135 h study time
Offer 1 with catalog number 4017610DNR for all students in the 1st semester at a (D) Master - preliminary level.
Titularis: Olivier Thas
Email: Olivier.Thas@UGent.be
Bibliografie http://lib.ugent.be/bibliografie/801001013507
Vakgroep Toegepaste wiskunde, biometrie en procesregeling (LA10)
Adres Coupure Links 653
9000 Gent
Telefoon 09 264 59 33
Fax 09 264 62 20
During the exercise sessions the students are coached by assistants.
Through the electronic learning environment (Minerva) students can exchange questions and answers outside lecture hours among themselves and with lecturers. Individual questions may be answered during a meeting with the lecturer after making an appointment. Slides and course notes are available. Further material is provided through Minerva.
J. Neter, M. Kutner, C. Nachstheim, W. Wasserman. 'Applied Linear Statistical Models', 4th edition. McGraw-Hill Education, 1996
In general, the course aims to reach the following end terms:
Knowledge: knowledge on basis statistical data analysis techniques
Skills: the student will be able to translate a research question into a statistical problem, which he/she can solve using basic statistical methods. In particular, these methods are related to the analysis of means (e.g. t-tests, ANOVA) and regression analysis. The student will be capable of performing the data analysis, and of interpreting the results, and he will be able to translate these conclusions back to the context of the original research question.
Emphasis is put on the exercises, most of which are on PC with statistical software. The examples and exercises are based on case studies relevant to the students work environment. In particular, examples are selected from food science, food technology, aquaculture and environmental sciences. The practicals are organised in groups. Depending on the number of students, the groups are made as homogeneous as possible in terms of the scientific interest of the students. Each group gets a different set of exercises so as to make the exercises as relevant as possible for each group.
Final competences:
The student understands the basics of statistical data exploration and statistical inference, and he/she can perform basic statistical data analyses. The student recognises important problems in the study design/analyses and knows how these may affect the conclusions from the statistical data analysis. The student is able to read and understand the "statistical methods" section in many of the papers in the subject field of food technology, nutrition and food sciences.
The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.
Within the Other Exam category, the following assignments need to be completed:
Theory: period aligned evaluation
Exercises: period aligned and non-period aligned evaluation
Students who eschew period aligned and/or non-period aligned evaluations for this course unit may be failed by the examiner.
Theory: written (open book) examination
The student has to make some data-analysis exercises for which he/she has to understand and apply the theory.
Exercises: written (open book) examination and written reporting about a project involving statistical data-analysis
The total mark is computed as an average of the marks for project (25%) and the written test (75%).
This offer is part of the following study plans:
Master of Sustainable Land Management: Urban Land Engineering