470-2404/02 – Introduction to Statistics (ZS)

Gurantor departmentDepartment of Applied MathematicsCredits4
Subject guarantorIng. Martina Litschmannová, Ph.D.Subject version guarantorIng. Martina Litschmannová, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
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.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 8+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

Project work


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

Presence form: Discussions: - 10 short tests during the semester per 2 points, 20 points overall (minimum required: 6 points) - 4 homeworks per 5 points, 20 points overall (minimum required: 5 points for each task) Exam: - 10 short tests during the semester per 2 points, 20 points overall (minimum required: 6 points) Combined form: Discussions: - 3 homeworks during the semester per 10 points, for a total maximum of 30 points (minimum required: 3 points for each homework) - Test with a maximum of 10 points (minimum required: 1 point) - Semester project, max 20 points (minimum required: 10 points) Exam: - written exam (practical part: max. 50 points, required minimum: 25 points, theoretical part: max. 10 points, required minimum: 2 points)


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. Students will pass the exam if they meet the the required minimum of each of the sub-tasks and compensatory gain (Discussions and Exam) at least 51 points.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1) Introduction to Probability Theory 2) Conditonal probability, Bayes Theorem 3) Discrete random variable 4) Discrete probability distributions 5) Continuous random variable 6) Continous probability distributions 7) Random Vector 8) Exploratory data analysis - qualitative variable and two qualitative variables 9) Exploratory data analysis - quantitative variable 10) Exploratory data analysis - two quantitative variables (independent variables vs. paired data) 11) Introduction to statistical induction, Introduction to estimation theory 12) Introduction to hypothesis testing 13) One sample tests of mean and binomial test of proportion

Conditions for subject completion

Combined form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  27
Mandatory attendence parzicipation: Participation at tutorials is recommended

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (B0914A060002) Biomedical technology P English Ostrava 3 Compulsory study plan
2019/2020 (B0914A060002) Biomedical technology K English Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner