Bi5045 Biostatistics for Computational Biology

Faculty of Science
Spring 2020
Extent and Intensity
3/1/0. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
RNDr. Tomáš Pavlík, Ph.D. (lecturer)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
Mgr. Michal Uher (seminar tutor)
Guaranteed by
RNDr. Tomáš Pavlík, Ph.D.
RECETOX - Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX - Faculty of Science
Mon 17. 2. to Fri 15. 5. Tue 8:00–11:50 A1/609 - IBA (A1,6.p, Kamenice 3)
None - it is a basic course.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The aim of this course is to introduce the students to the field of biostatistics, its principles and methodology, and to teach them how to analyze data regarding real biological and clinical problems.
Learning outcomes
At the end of the course, students are able to:
- understand biostatistical terminology,
- formulate statistical hypothesis according to given type of data,
- design and optimize biological and clinical experiments,
- apply statistical methods and tests according to given type of data,
- verify assumptions of the statistical methods and tests,
- properly interpret his or her own results,
- critically assess already published results.
  • 1. Introduction to biostatistics. Examples of problems addressed with biostatistical methods.
  • 2. Mutual relationship among probability theory, statistics and biostatistics.
  • 3. Data types, their description and visualization.
  • 4. Random variable, probability distributions and its characteristics, real data.
  • 5. Introduction to estimation theory. The principles and criteria for deriving statistical estimates.
  • 6. Various estimators of parameters of random variables.
  • 7. Introduction to hypotheses testing. Logic of hypotheses testing and related terms.
  • 8. Parametric and nonparametric testing of hypotheses regarding quantitative random variables.
  • 9. Analysis of variance (ANOVA).
  • 10. Testing hypotheses with binary and categorical random variables. Goodness of fit tests.
  • 11. Experimental design. Required sample size determination for hypothesis tests.
  • 12. Correlation and regression analysis. Correlation and causality. Linear regression model.
    recommended literature
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Sokal, R.R., Rohlf, F.J. (1994) Biometry, W. H. Freeman, 3th ed.
Teaching methods
Theory and practical excercises.
Students are encouraged to actively participate on the discussed issues as well as they are encouraged to ask questions and comment on examples.
Assessment methods
This course is finished by written exam aimed on principles and correct application of methods for solving of practical examples.
There are two short tests during the semester that are included in the final evaluation.
Language of instruction
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019.
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