LMAF10065U Applied Statistics
Volume 2013/2014
Education
MSc Programme in
Agriculture
Content
Each student carries out a statistical
project (in a group) related to an experiment or a numerical
investigation preferably delivered by one of the students in the
group. A report is written in journal style and presented orally.
Besides, a small number of statistical themes are taught. Examples
of such themes are multi-way ANOVA, random effects, analysis of
repeated measures and cross-over trials, analysis of count data or
ordinal data, analysis of data with detection limit, simulation
methods, non-linear regression models, analysis of time series
data, Markov models. There is also some discussion of statistical
methods used in specific application areas such as bacterial
counting, plant competition and human nutrition. The statistical
themes as well as the application areas may vary somewhat from year
to year and to some extent adapt to the interests of the
students.
Learning Outcome
The course aims
at giving the student experience of carrying out statistical
analyses. The topics (methods) taught may vary from year to year.
After completing the course the student should be able to:
Knowledge:
- recognize certain data types, identify and specify appropriate statistical models, and argue for the appropriateness
- explain the prerequisities, prospects and limitations of the methods
Skills:
- formulate relevant problems and choose an appropriate statistical model addressing these problems
- carry out the actual analysis (computations). This includes model fitting, model validation and hypothesis testing.
- extract relevant estimates, draw conclusions and communicate the results from the analysis
- use the statistical programming language R to carry out the analyses
Competences:
- independently formulate biological questions - motivated by data of similar types as those presented in the course - and answer them by the use of statistical methods.
After completing the course the student should be able to:
Knowledge:
- recognize certain data types, identify and specify appropriate statistical models, and argue for the appropriateness
- explain the prerequisities, prospects and limitations of the methods
Skills:
- formulate relevant problems and choose an appropriate statistical model addressing these problems
- carry out the actual analysis (computations). This includes model fitting, model validation and hypothesis testing.
- extract relevant estimates, draw conclusions and communicate the results from the analysis
- use the statistical programming language R to carry out the analyses
Competences:
- independently formulate biological questions - motivated by data of similar types as those presented in the course - and answer them by the use of statistical methods.
Teaching and learning methods
During the first half of the
course lectures and practical (computer) exercises will run
parallel with the initial part of the project work, while the
second half will concentrate on the projects. In the last week the
students will present their projects orally and give critique to
one of the other projects.
Workload
- Category
- Hours
- Exam
- 2
- Guidance
- 5
- Lectures
- 20
- Practical exercises
- 20
- Preparation
- 69
- Project work
- 80
- Theory exercises
- 10
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignmentOral examination, 30 minutesDescription of Examination: Each group writes a report in a journal paper format about their project. At the end of the course each student is examined individually. The grade is awarded on the basis of an overall assessment of the report and the oral exam.
- Aid
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assesment
Quality of written report with focus on describing and using
appropriate statistical methods. Quality of oral presentation with
focus on presenting key methodology and results.
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- LMAF10065U
- Credit
- 7,5 ECTS
- Level
- BachelorFull Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- A
- Course capacity
- 25
- Continuing and further education
- Study board
- Study Board of Natural Resources and Environment
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Christian Ritz (ritz@nexs.ku.dk)
Saved on the
30-04-2013