| Course Unit Code | 470-2404/03 |
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| Number of ECTS Credits Allocated | 2 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | First Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| LIT40 | Ing. Martina Litschmannová, Ph.D. |
| Summary |
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| Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. This course will teach students the basic concepts used to describe data. With the knowledge gained in this course, students will be ready to undertake their first very own data analysis using the open source software R, which is rapidly becoming the leading programming language in statistics and data science. |
| Learning Outcomes of the Course Unit |
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This subject is an introductory course of statistics. The aim of the course is to develop sufficient knowledge of statistical tools and procedures, understanding of the underlying theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into other areas of his or her own work.
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| Course Contents |
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1. Introduction to software R – I. (basics of this open source language, including factors, lists and data frames)
2. Introduction to software R - II. (methods of description and visual representation of categorical data)
3. Association between two categorical variables (pivot tables, description statistics, visualization -2 exercises)
4. Methods of description and visual representation of quantitative data
5. Association between two quantitative variables (correlation coefficients, scatter plot, paired data – Bland-Altmann method)
6. Data Manipulation in R (data import and export, how to merge and split file using R, …)
7. An example of statistical data analysis in R – real data (I.)
8. An example of statistical data analysis in R – real data (II.)
9. Descriptive analysis of a time series
10. Excel tips and tricks - I. (introduction to data analysis in MS Excel – relative and absolute cell references, named ranges)
11. Excel tips and tricks - II. (pivot tables)
12. Excel tips and tricks - III. (array formulas, data verification, indirect function)
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| Recommended or Required Reading |
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| Required Reading: |
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[1] CRAWLEY, Michael J. Statistics: an introduction using R. Chichester, West Sussex, England: J. Wiley, c2005. ISBN 978-0470022986
[2] StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com
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[1] LITSCHMANNOVÁ, M. (2011), Úvod do statistiky, elektronická skripta, dostupné online z: http://mi21.vsb.cz/modul/uvod-do-statistiky
[2] CRAWLEY, Michael J. Statistics: an introduction using R. Chichester, West Sussex, England: J. Wiley, c2005. ISBN 978-0470022986 |
| Recommended Reading: |
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| [1] Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. |
[1] PAVLÍK, T., DUŠEK, L. (2012): Biostatistika, Akademické nakladatelství CERM, ISBN 978-80-7204-782-6
[2] ZVÁROVÁ, J. (2016, 3. vydání): Základy statistiky pro biomedicínské obory I., Karolinum, ISBN 978-80-246-3416-6
[3] StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit | Credit | 40 (40) | 20 |
| Project 1 | Project | 10 | 2 |
| Project 2 | Project | 10 | 2 |
| Project 3 | Project | 10 | 2 |
| Project 4 | Project | 10 | 2 |