E5540c Biostatistics - practices

Faculty of Science
Autumn 2025
Extent and Intensity
0/1/0. 1 credit(s) (fasci plus compl plus > 4). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
Mgr. Hana Kučerová (lecturer)
RNDr. Simona Littnerová, Ph.D. (lecturer)
Mgr. Klára Benešová (seminar tutor)
RNDr. Michaela Cvanová, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
Mgr. Renata Chloupková (seminar tutor)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Denisa Krejčí, Ph.D. (seminar tutor)
RNDr. Tereza Nečasová (seminar tutor)
Mgr. Ondřej Ngo, Ph.D. (seminar tutor)
RNDr. Michal Svoboda (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable of Seminar Groups
E5540c/01_1: each even Thursday 8:00–9:50 D29/347-RCX2, S. Littnerová
E5540c/01_2: each odd Thursday 8:00–9:50 D29/347-RCX2, H. Kučerová
E5540c/02_1: each even Thursday 10:00–11:50 D29/347-RCX2, S. Littnerová
E5540c/02_2: each odd Thursday 10:00–11:50 D29/347-RCX2, H. Kučerová
Prerequisites
NOW( E5540 Biostatistics - basic course )
Bi5040 Biostatistics in the same semester.
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 9 fields of study the course is directly associated with, display
Course objectives
The course is basic introduction into practical data analysis for students of biology and clinical study specialisations. The course accompanies lectures of Bi5040 Biostatistics and shows the computation of presented methods on PC using statistical software (descriptive statistics, one sample and two sample tests, categorical data analysis, ANOVA, correlation and regression analysis, data visualisation, basics of experimental design).
Learning outcomes
The students will be able after the course to use the folowing data analysis methods:
Descriptive statistics, data visualisation.
Tables of distribution functions.
Introduction to sampling design and experimental design.
Distribution of continuous and bivariate variables.
Application of binomial distribution in biology.
One sample testing.
Two sample testing.
Application of goodness-of-fit test in biology.
Analysis of variance (ANOVA), non - parametric ANOVA.
Corelation, linear regression.
Syllabus
1. Data processing – principles of correct data manipulation. MS Office Excel – appropriate tool for storing, organizing, and manipulating data. • 2. Introduction to statistics. Data types in medicine and biology; nominal, ordinal, continuous variable. Visualization of quantitative and qualitative (categorical) variables. • 3. Descriptive statistics. Mean, median, quantiles, variance. Frequency table. • 4. Distribution of continuous variables. Normal distribution, log-normal distribution. • 5. Principles of hypotheses testing. Definition of null and alternative hypothesis. Significance level. Type I and type II error. • 6. Graphical examining of normal distribution (histogram, normal-probability plot). Shapiro-Wilk test – a test of normality. • 7. Parametrical tests: t-tests. One-sample t-test, two-sample t-test, t-test for dependent samples. • 8. Analysis of variance ANOVA. • 9. Nonparametrical tests: one-sample Wilcoxon test, Mann-Whitney U test, Wilcoxon test for dependent samples, Kruskal-Wallis test. • 10. Definition of contingency table and its analysis: Pearson chi-square test, Fisher exact test, McNemar test. • 11. Correlation. Pearson correlation coefficient, Spearman correlation coefficient. • 12. Introduction to regression analysis. Linear regression.
Literature
    recommended literature
  • Petrie, A., Watson, P. (2006) Statistics for Veterinary and Animal Science, Wiley-Blackwell; 2nd ed
  • 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
Training in statistical software on computers supported by PowerPoint presentation; students are encouraged to discussion.
Assessment methods
Exam on computers based on correct application of statistical methods on example data.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (recent)
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