Course: Biostatistics

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Course title Biostatistics
Course code KBO/759
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Frequency of the course In each academic year, in the winter semester.
Semester Winter
Number of ECTS credits 5
Language of instruction English
Status of course Compulsory
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Blažek Petr, RNDr. Ph.D.
Course content
Content of lectures: Introduction to statistics Basic descriptive statistics Probability and likelihood Testing of hypotheses Goodness-of-fit test, contingency tables t-tests and their non-parametric counterparts Analysis of variance and its non-parametric counterparts Correlation and linear regression Multiple regression and general linear models Non-linear regression Introduction to multivariate methods Content of practicals: Practicals are focused on computing of statistical methods theoretical principles of which are covered by the lectures. In addition, graphical presentation of data and analysis results is emphasised. All computations are carried out in R package.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Work with multi-media resources (texts, internet, IT technologies)
  • Preparation for classes - 20 hours per semester
  • Preparation for exam - 20 hours per semester
  • Preparation for credit - 20 hours per semester
  • Class attendance - 52 hours per semester
Learning outcomes
The aim of the course is to introduce the principles of statistical thinking and the use of statistics in science. In addition to this theoretical background, special emphasis is put on practical use of statistical analyses for data processing. After completing the course, the students should be able to process their own data and apply basic statistical methods to test hypotheses related to topics of their bachelor these
Student can think in the context of statistical principles (experimental design, logic of scientific work), knows basic statistical methods (see Content), understands their results when reading publications, and is able to perform them independently in R language.
Prerequisites
Basic knowledge of math and MS Excel at the high-shool level.

Assessment methods and criteria
Written examination, Essay, Interim evaluation

The evaluation consists of three parts: - mid-term test (20 points) - essay (20 points) - final test (60 points) Points from these three parts are summed up, a minimum of 50 points is required to pass the course. The essay submission is considered to be the pre-exam credit, i.e. students are not allowed to take final test if they did not submit the essay. The tests are open-textbook exams, focused on the basic theory and on the practical use of statistical methods using R. The essay should resemble very brief scientific publication / bachelor thesis, where students use some methods they learned in this course. Details are published on the course webpage (see Content).
Recommended literature
  • Crawley M.(2007): The R Book. John Wiley & Sons Ltd, Chichester..
  • Materials available on the course website.
  • Paradis E. (2005): R. for beginners. Institut des Sciences de l'Evolution Université Montpellier II, Montpellier..
  • Yakir B. (2011): Introduction to Statistical Thinking (With R, Without Calculus). The Hebrew University, Jerusalem..
  • Beckerman A.P., Petchey O.L. Getting started with R. An introduction for biologists.. Oxford University Press, Oxford, 2012. ISBN 987-0-19-960162.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Biological Chemistry (1) Category: Chemistry courses 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Bioinformatics (1) Category: Informatics courses 3 Recommended year of study:3, Recommended semester: Winter