MNBS081p Biostatistics - lecture

Faculty of Medicine
spring 2022
Extent and Intensity
1/0/0. 2 credit(s). Type of Completion: k (colloquium).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Michal Svoboda (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
MVDr. Halina Matějová (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Contact Person: MVDr. Halina Matějová
Supplier department: Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Timetable
Tue 15. 2. 14:00–15:40 F01B1/709, Tue 22. 2. 14:00–15:40 F01B1/709, Tue 1. 3. 14:00–15:40 F01B1/709, Tue 8. 3. 14:00–15:40 F01B1/709, Tue 15. 3. 14:00–15:40 F01B1/709, Tue 22. 3. 14:00–15:40 F01B1/709, Tue 29. 3. 14:00–15:40 F01B1/709, Tue 5. 4. 14:00–15:40 F01B1/709, Tue 12. 4. 14:00–15:40 F01B1/709, Tue 19. 4. 14:00–15:40 F01B1/709, Tue 26. 4. 14:00–15:40 F01B1/709, Tue 3. 5. 14:00–15:40 F01B1/709, Tue 10. 5. 14:00–15:40 F01B1/709, Tue 17. 5. 14:00–15:40 F01B1/709, Tue 24. 5. 14:00–15:40 F01B1/709
Prerequisites
none - basic course
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course is aimed on applied data analysis for students of biological and clinical sciences. The presented topics range from theoretical background (statistical estimates, statistical distributions, statistical hypothesis testing) and simple applications (one sample and two sample tests, correlation analysis) to stochastic modelling (experimental design, regression analysis, analysis of variance).
Learning outcomes
At the end of the course the students are able to:
- define structure of dataset for statistical analysis;
- visualize the data and interpret data visualisation;
- identify correct methods of descriptive statistics;
- formulate hypothesis for statistical testing;
- select the correct statistical tests for hypotheses confirmation/refusal;
- interpret results of statistical evaluation, both analysis of own data and statistics in scientific literature;
- assess the applicability of statistical methods on various types of data.
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
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
Teaching methods
lectures supplemented by examples of practical exercises
Assessment methods
The course is finished by written exam aimed on principles, prerequisites and correct selection of methods for solution of practical examples.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Information on the extent and intensity of the course: 15.
The course is also listed under the following terms Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, spring 2019, spring 2020, spring 2021, spring 2023, spring 2024, spring 2025.
  • Enrolment Statistics (spring 2022, recent)
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