Lecturer(s)


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, nonnormal distr., Student's tdistribution, Pearson's ChiSquare distr., Fisher's Fdistribution. 4.Estimation of population parameters, confidence intervals for the mean value, standard deviation and median. Testing of statistical hypotheses. 5.Parametric tests. Ftest, Student's ttest. 6.Relations between two variables. Regression analysis  simple linear regression. Correlation analysis. Significance of the correlation coefficient. Nonlinear 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 (Ftest). Examples. 5.Revision practice: Descriptive characteristics, Tests for variance hypotheses. Examples. 6.Parametric tests(ttest, Ftest). Examples. 7.MS Excel  Data files processing: Ftest, Student's ttest. Graphic presentation. 8.Parametric tests in MS Excel (Ftest, ttest) practical examples 9.MS Excel Statistical data files processing: basic statistic parameters, Ftest, Student's ttest. Individual practice (Model examples I). 10.MS Excel  Statistical data files processing: correlation and regression analysis. Model examples. 11.Model situations in veterinary medicine: Ftest, ttest individual practice (Model examples II  MS Excel). 12.Model situations in veterinary medicine  credit task: Ftest, ttest  individual practice (Example  MS Excel). 13.Credit.

Learning activities and teaching methods

unspecified

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.

Prerequisites

unspecified

Assessment methods and criteria

unspecified
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 0333960440.

Bedáňová, I. Basics of Statistics for Students of Veterinary Medicine. VFU Brno, 2007. ISBN 9788073050221.
