210001 Applied Statistics

Details
Responsible DepartmentDepartment of Basic Science and Environment

Earliest Possible YearBSc. 2 year to MSc. 2 year
DurationOne block
 
Credits7.5 (ECTS)
 
Level of CourseJoint BSc and MSc
 
ExaminationFinal Examination

oral examination


All aids allowed

Description of Examination: Each group writes a report in a journal paper format about their project and presents it orally in class. At the end of the course each student is examined individually.

Weight: Oral exam in report and curriculum 100%.



pass/fail, internal examiner
 
Requirement for Attending ExamThe student should make a presentation in class of an exercise.
 
Organisation of TeachingLectures, (computer) exercises and supervised project work. Preferably the project is about the student's own data. Report writing in journal article style and oral presentations.
 
Block PlacementBlock 2
Week Structure: A
 
Language of InstructionEnglish
 
Optional Prerequisites210005 Statistical Data Analysis 1
The student should have at least one statistics course. Moreover, the student should have experience with SAS or R.
 
Restrictions30.
 
Course 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.
 
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.
 
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 a statistical programming language such as R or SAS 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.
 
Course Literature
Anders Tolver Jensen and Helle Sørensen:
"Lecture notes for Applied Statistics", 2007.

Possibly, supplementary material as handouts.
 
Course Coordinator
Christian Bressen Pipper, pipper@life.ku.dk, Department of Basic Sciences and Environment/Statistik, Phone: 353-32344
 
Study Board
Study Committee NSN
 
Work Load
lectures20
practicals20
theoretical exercises10
supervision5
examination2
preparation69
project work80

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