Experimental Design and Statistical Methods in Biology (StatBio)
Practical information  

Study year  2016/2017 
Time 
Block 3 
Programme level  Full Degree Master 
ECTS  7,5 ECTS 
Course responsibles  
 

Course content
The course is intended to give a broad overview of experimental designs and statistical methods in order for students to plan their own experiments and to analyze existing data. Students are recommended to follow the course shortly before they start their bachelor project, their M.Sc. thesis project, or as a part of a Ph.D. study programme.
Learning outcome
Knowledge:
The student will get an overview of a range of statistical concepts and tools:
 Regression
 ANOVA
 Interaction between factors
 Design considerations
 Model check and fit, data representation
 Systematic and random effects
 Logistic regression
 Contingency tables
 Use of statistical software (SAS)
Skills:
A student that has successfully finished the course will possess the following qualifications:
 be able to select the appropriate statistical model for the design in question.
 be able to apply General Linear Models (analysis of variance (ANOVA), analysis of covariance (ancova), polynomial and multiple regression, nested and mixed anovas)
 be able to describe multivariate anova (manova), repeated measurements anova, logistic regression and loglinear models.
 be able to develop and apply statistical models that incorporate qualitative (both fixed and random effects) and quantitative variables, main effects, interactions, and second or higher order terms.
 be able to use statistical software (currently SAS) to load a data set, to sort and summarize data, to perform relevant statistical analyses, and to report the results either graphically or in tables.
 be able to estimate the parameters of a statistical model and their standard errors, and to test whether they are significantly different from 0.
 be able to identify significant and nonsignificant factors so as to simplify the statistical model using various criteria for best fit.
 be able to apply a priori and a posteriori tests to identify treatment differences.
 be able to use the statistical model as a predictive tool to forecast the expected outcome of an observation from a set of independent variables.
 be able to check whether data meet the assumptions of the model and, if needed, to select an appropriate data transformation.
Competences:
The student will learn the most commonly used experimental designs and appreciate their advantages with respect to the subsequent statistical analysis of data. The student will be able to select or, if necessary, to develop a statistical model for the experimental design, state the relevant statistical hypotheses, conduct the statistical analysis (generally using statistical software), present the results in a clear and understandable way, and finally interpret the results in a biological context to reach a sound conclusion based on the empirical evidence. In addition, the student should possess the necessary theoretical insight in statistics to be able to understand and comment critically on the use of statistics by others.
Recommended prerequisites
The students are assumed to possess a basic knowledge of statistics at a level corresponding to at least “Matematik/Statistik” (1st year of bachelor study). The time schedule does not allow for repetition of basic statistics, so students lacking an uptodate knowledge are requested to refresh fundamental statistical concepts and principles prior to the course. Although the course puts emphasis on applying statistics, it is unavoidable that some theory will be encountered, so students with poor mathematical skills should consider whether the course fulfils their needs.Sign up
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Education
MSc Programme in Biology
MSc Programme in Biochemistry
Studyboard
Study Board of Biology and Animal ScienceCourse type
Single subject courses (day)Duration
1 blockSchedulegroup
Teaching and learning methods
Lectures: 36 hours (2+2 hours per week for 9 weeks, of which c. 1 hour per week is student presentations). Computer exercises: 27 hours (3 hours per week for 9 weeks).Capacity
55Language
EnglishLiterature
See Absalon.
Workload
Category  Hours 
Preparation  129 
Lectures  24 
Colloquia  12 
Practical exercises  27 
Project work  14 
English  206 
Exam
Type of assessment
1) During the course each student has to give a short (1215 minutes) individual presentation of a case study that has involved statistical analysis. The study may either be taken from literature or be his/her own project.
2) Students should, in groups of 2 or 3 hand in three homework exercises of an acceptable standard.
3) The student will be evaluated on his/her active participation at both the lectures and the exercises
4) The student will be evaluated on the quizzes presented at lectures.
The evaluation is based on an overall assessment of the four subsubparts.
Marking scale
passed/not passedCriteria for exam assessment
See Learning Outcome
Censorship form
No external censorshipReexam
 The submission of completed exercises, with themes provided by the lecturers 5 days before the examination, of a sufficiently high standard to demonstrate understanding of the field
 An individual oral presentation of 10 minutes duration and 10 min questioning, of relevant aspects of experimental design and statistical analysis in a scientific paper chosen by the teachers, with a 3 day preparation period (all aids allowed during the exam).
The response to each part of the reexamination (presentation+ exercises) should be of sufficient standard in order to pass.
If the student has not passed the exam registration requirements then the oral reexam will be 30 minutes instead of 20 minutes. During the extra 10 minutes the student must make a presentation of a theme that the course responsible has chosen. The theme will be chosen from one of the lectures where the student was absent. The preparation time is unchanged (3 days).
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