MFST081p Statistics - lecture

Faculty of Medicine
spring 2021
Extent and Intensity
1/0/0. 2 credit(s). Type of Completion: zk (examination).
Taught online.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
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: Mgr. Leona Dunklerová
Supplier department: Institute of Biostatistics and Analyses - Other Departments for Educational and Scientific Research Activities - Faculty of Medicine
Timetable
Thu 4. 3. 15:00–16:40 D29/347-RCX2, Thu 11. 3. 15:00–16:40 D29/347-RCX2, Thu 18. 3. 15:00–16:40 D29/347-RCX2, Thu 25. 3. 15:00–16:40 D29/347-RCX2, Thu 1. 4. 15:00–16:40 D29/347-RCX2, Thu 8. 4. 15:00–16:40 D29/347-RCX2, Thu 15. 4. 15:00–16:40 D29/347-RCX2, Thu 22. 4. 15:00–16:40 D29/347-RCX2, Thu 29. 4. 15:00–16:40 D29/347-RCX2, Thu 6. 5. 15:00–16:40 D29/347-RCX2, Thu 13. 5. 15:00–16:40 D29/347-RCX2, Thu 20. 5. 15:00–16:40 D29/347-RCX2, Thu 27. 5. 15:00–16:40 D29/347-RCX2, Thu 3. 6. 15:00–16:40 D29/347-RCX2, Thu 10. 6. 15:00–16:40 D29/347-RCX2
Prerequisites
no - 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 aim of the course is to provide students with basic principles of statistical analysis of biological data from the experimental design, data collection and visualisation to descriptive statistics and statistical hypotheses testing.
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
  • ZAR, Jerrold H. Biostatistical analysis. Fifth edition. Uttar Pradesh, India: Pearson India Education Services, 2014. 756 stran. ISBN 9789332536678. info
  • GERYLOVOVÁ, Anna and Jan HOLČÍK. Úvod do statistiky. Text pro semináře. 2. vyd. Brno: Masarykova univerzita, 2000. 31 pp. ISBN 80-210-2301-5. info
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
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
Further comments (probably available only in Czech)
Study Materials
Information on the extent and intensity of the course: 15.
The course is also listed under the following terms Spring 2000, Spring 2001, Spring 2002, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, spring 2019, spring 2020.
  • Enrolment Statistics (recent)
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