Course: Biostatistics

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Course title Biostatistics
Course code 2390/HASV
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 4
Language of instruction English
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
  • Bedáňová Iveta, doc. RNDr. Ph.D.
  • Blahová Jana, doc. Ing. Ph.D.
Course content
Lectures: 1.Types of statistical data. Population and sample. Characteristic of variables, frequency distribution, probability distribution, quantiles. 2.Descriptive statistics - measures of central tendency and measures of dispersion and variability. 3.Probability distributions: Gaussian normal, non-normal distr., Student's t-distribution, Pearson's Chi-Square distr., Fisher's F-distribution. 4.Estimation of population parameters, confidence intervals for the mean value, standard deviation and median. Testing of statistical hypotheses. 5.Parametric tests. F-test, Student's t-test. 6.Relations between two variables. Regression analysis - simple linear regression. Correlation analysis. Significance of the correlation coefficient. Non-linear regression. 7.Categorical data, estimation of frequencies. Test for difference between empirical and theoretical frequency, testing for difference between 2 empirical frequencies. Categorical data relationship, contingency tables. Practices: 1.Introduction practice. 2.Descriptive statistics - calculations: arithmetic mean, median, mode, range, variance, standard deviation, coefficient of variation (examples). 3.Statistical tools in MS Excel. Data files processing: basic statistic parameters. Examples 4.Statistical testing: Tests on variance hypotheses (F-test). Examples. 5.Revision practice: Descriptive characteristics, Tests for variance hypotheses. Examples. 6.Parametric tests(t-test, F-test). Examples. 7.MS Excel - Data files processing: F-test, Student's t-test. Graphic presentation. 8.Parametric tests in MS Excel (F-test, t-test)- practical examples 9.MS Excel- Statistical data files processing: basic statistic parameters, F-test, Student's t-test. Individual practice (Model examples I). 10.MS Excel - Statistical data files processing: correlation and regression analysis. Model examples. 11.Model situations in veterinary medicine: F-test, t-test -individual practice (Model examples II - MS Excel). 12.Model situations in veterinary medicine - credit task: F-test, t-test - individual practice (Example - MS Excel). 13.Credit.

Learning activities and teaching methods
Learning outcomes
Statistics represents one of the basic disciplines, that are an inevitable part of the education in all the biological, medical and related sciences, and consequently in the veterinary medicine. The importance of statistics results from principles of collecting, processing, presentation and interpretation of biological and medical data, when much of knowledge and experience generation would be erroneus and incorrect without a statistical analysis. A practical consequence of the statistics is shown especially in the research and development sphere in the medical disciplines, as well as in the clinical veterinary practice and hygiene and ecology sphere in the course of food inspection. The aim of the statistics education is to achieve a qualification for an individual analysis of particular problems in veterinary medicine with the aid of biostatistics and for a practical skill of some common and special procedures in the PC applying in the sphere of the statistical analysis. Biostatistical knowledges can be useful in final theses and diploma woks as early as in the course of the study.


Assessment methods and criteria
Credit: - participation in 11 practices (at least) in the course of semester (from 13 possible practices) - protocols from practices Exam: - credit awards (practices) - theoretical knowledge in the subject Statistics & Informatics (PC test)
Recommended literature
  • Ashcoft, S., Pereira, Ch. Statistics for the biological sciences. Palgrave MacMillan, GB, 2003. ISBN 0-333-96044-0.
  • Bedáňová, I. Basics of Statistics for Students of Veterinary Medicine. VFU Brno, 2007. ISBN 978-80-7305-022-1.

Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Veterinary Hygiene and Ecology - (12) Veterinary medicine and veterinary prevention 1 Winter