NBIC66 · 6 credits · Basic level (G2F) · Biologi
Analysis of Biological Data Using Statistical Models
Active · next start HT 2026
80%
passed the latest ordinary exam
TEN1 · 28 Oct 2025 · 30 students
76%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 54 students
56%
Harder than 56% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 54 students.
Part of 2 programmes
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
76% passed · 54 students
- 80%30
- 71%24
Re-exams
92% passed · 12 students
- 83%6
- 100%6
- Pass the re-exam: 92% (12 students), compared with 76% (54 students) on the ordinary exam
- Get 4 or 5: 36% (66 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 50%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Written assignments
- G
- U
Main rounds
78% passed · 67 students
- 84%37
- 70%30
Later rounds
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
About the course
Aim
The goal of the course is to give the skills to use modern quantitative methods to analyze biological data with the use of statistical models.
Upon course completion, the student should be able to
- Independently handle large biological datasets in R for analysis with appropriate statistical methods.
- Identify and explain statistical concepts and theories about relationships between variables.
- Carry out statistical analysis of relationships between variables using parametric and non-parametric methods.
- Report conclusions from completed statistical analysis and critically examine these conclusions in their context.
Content
- Comparisons of more than two populations using parametric and non-parametric methods, for example ANOVA and Kruskal-Wallis.
- Multiple comparisons with family-wise confidence.
- Linear regression models with one and several explanatory variables.
- Linear non-parametric regression, for example Theil-Sen.
- Introduction to generalized linear models, for example logistic regression.
- Map functions in R to repeat tests.
- Multiple analysis in R.
- Examiner
- György Barabas
- Scheduled hours
- 44 h
- Main field of study
- Biologi