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
Year3Semesterwinter
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
Part-time 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

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

Discussion Full-time study: - homeworks/intermediate tests (max. 20 points overall, required minimum: 20 points) Conditions for credit: For successful completion of the Discussions is given credit. Students will receive credit if they gain at least 20 points. Exam - written exam (max. 60 points, required minimum: 30 points) Conditions for taking the exam: Students will pass the exam if they meet the the required minimum of the exam and compensatory gain (Discussions and Exam) at least 51 points.

E-learning

Other requirements

Full-time study Participation at all discussions is obligatory, 2 apologies are accepted. Participation at lectures is recommended, knowledge of lecture materials is a prerequisite for participation at the exercises. Combined study Participation at all tutorials is obligatory, 1 apology is accepted.

Prerequisities

Subject has no prerequisities.

Co-requisities

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

Full-time form (validity from: 2022/2023 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  30 3
Mandatory attendence participation: Participation at all exercises is obligatory, 2 apologies are accepted. Participation at lectures is recommended, knowledge of lecture materials is a prerequisite for participation at the exercises.

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Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines.

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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2023/2024 (B0714A060017) Biomedical Technology P English Ostrava 3 Compulsory study plan
2022/2023 (B0714A060017) Biomedical Technology P English Ostrava 3 Compulsory study plan
2022/2023 (B0714A060017) Biomedical Technology K English Ostrava 3 Compulsory study plan
2021/2022 (B0714A060017) Biomedical Technology P English Ostrava 3 Compulsory study plan
2021/2022 (B0714A060017) Biomedical Technology K English Ostrava 3 Compulsory study plan
2020/2021 (B0714A060017) Biomedical Technology K English Ostrava 3 Compulsory study plan
2020/2021 (B0714A060017) Biomedical Technology P English Ostrava 3 Compulsory study plan
2019/2020 (B0714A060017) Biomedical Technology P English Ostrava 3 Compulsory study plan
2019/2020 (B0714A060017) Biomedical Technology K English Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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