Bi5046 Biostatistics for Computational Biology and Biomedicine

Faculty of Science
Spring 2021
Extent and Intensity
2/1/0. 3 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
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.
At the end of the course students should be able to:
formulate statistical hypothesis according to given type of data;
design and optimize biological and clinical experiments;
apply analytical methods and verify correctness of their use;
understand statistical terminology and read scientific papers;
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.
  • 13. Introduction to stochastic modelling. Model and its components.
  • 14. Introduction to survival analysis. Censoring principle. Parametric and nonparametric survival function estimates.
  • Sokal, R.R., Rohlf, F.J. (1994) Biometry, W. H. Freeman, 3th ed.
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
Teaching methods
Students are encouraged to actively participate on the discussed issues as well as they are encouraged to ask questions and comment on examples.
Student attendance is expected.
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
The course is taught annually.
The course is taught: every week.
Teacher's information
The course is also listed under the following terms Spring 2020.
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