E0410 Fundamentals of Statistics for Scientific Data Using R

Faculty of Science
Spring 2024
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)
Daria Sapunova (seminar tutor)
Guaranteed by
Mgr. et Mgr. Jiří Kalina, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. et Mgr. Jiří Kalina, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 19. 2. to Sun 26. 5. Thu 9:00–10:50 D29/347-RCX2
Prerequisites
Basic math (high-school level). No need of statistical knowledge.
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
Course objectives
The aim of the course is to provide students with the essentials of common scientific statistical background and fundamental R programming skills.
Learning outcomes
After completing the course, students will be able to use R for solving common scientific data analytical tasks (designing data collection, data handling, elementary statistical tests and graphs, linear modeling) and further develop using tools provided in the course.
Syllabus
  • 1. Course description (content, assesment etc.). Basic statistical definitions, descriptive statistics. Practical in R, familiarization with RStudio, uploading data, basic calculations. 2. Descriptive statistics. Outliers, missing values. Practical in R. 3. Visualization - common graphs,aesthetics. Practical in R. 4. Data cleaning, Tidyverse package in R. Practical in R. Inspection and visualization of a dataset, results communication. 5. Hypothesis testing, p-values, distributions, data normality testing, data transformation. Practical in R. 6. Parametric and non-parametric statistics, parametric tests (t-test, ANOVA). Practical in R performing the tests, interpretation of the results. 7.Non-parametric tests (Kruskal-Wallis, Mann-Whitney). Practical in R performing the tests, interpretation of the results. 8. Pearson and Spearman correlation. Practical in R. 9. Linear regression (OLS). Practical in R. 10. Linear regression (OLS). Assumptions, model evaluation. Practical in R. 11. Multiple regression. Variables selection for a model, preparation of the model – good practice steps. Practical in R.
Literature
  • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
Teaching methods
Each lesson is divided into a theoretical part with a following practical part.
Assessment methods
Final at the end of the course - working on a dataset provided by a student or the teacher. The results should be presented as a pdf file consisting of a small introduction regarding the topic, statistical method description, data visualization and interpretation of the results.
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
Practical parts are held in R statistical environment (freely available).
The course is also listed under the following terms Spring 2025.
  • Enrolment Statistics (Spring 2024, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2024/E0410