470-2404/03 – Introduction to Statistics (ZS)

Gurantor departmentDepartment of Applied MathematicsCredits2
Subject guarantorIng. Martina Litschmannová, Ph.D.Subject version guarantorIng. Martina Litschmannová, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semestersummer
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
LIT40 Ing. Martina Litschmannová, Ph.D.
SIM46 Mgr. Lenka Přibylová, Ph.D.
ULC0011 Ing. David Ulčák
VRT0020 Mgr. Adéla Vrtková
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit 0+2
Part-time Credit 0+8

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Lectures
Tutorials
Project work

Summary

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.

Compulsory literature:

[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

Recommended literature:

[1] Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

Way of continuous check of knowledge in the course of semester

4 homeworks per 10 points, 40 points overall (minimum required: 5 points for each task)

E-learning

Další požadavky na studenta

For successful completion of the Discussions is given credit. Students will receive credit if they meet the required minimum of each of the sub-tasks and compensatory gain at least 20 points.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

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)

Conditions for subject completion

Part-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit Credit 40  20
Mandatory attendence parzicipation: Participation at all tutorials is recommended.

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2019/2020 (B0714A060010) Telecommunication Technology K Czech Ostrava 2 Compulsory study plan
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2019/2020 (B0713A060005) Electrical Power Engineering P Czech Ostrava 2 Compulsory study plan
2019/2020 (B0714A060012) Applied Electronics P Czech Ostrava 2 Compulsory study plan
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Occurrence in special blocks

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