470-2404/01 – 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 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.
VRT0020 Mgr. Adéla Vrtková
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

Discussions: A student may earn a maximum of 40 points for graded assignments during the semester. The minimum number of points for credit is 20. Full-time study: In the first part of the semester, each student will complete three homework assignments (1P - 3P) focused on probability theory (max. 20 points, min. 6 points). In the second part of the semester, students will complete four homework assignments (1S - 4S) focused on descriptive statistics. Each of the assignments will be assessed with a maximum of 5 points. To be awarded credit, a minimum of 10 points must be obtained from these assignments, i.e. max. 20 points, min. 10 points. You will be informed about the method of assignment and the deadline for submission of each assignment in LMS Moodle. The results will be recorded in Edison in aggregate, always after marking the relevant set of homework (1P - 3P and 1S - 4S). Combined study: Students will complete 4 homework assignments during the semester with a maximum of 10 points, for a total maximum of 40 points (required minimum: 3 points per homework assignment). Exam: - Written exam (max. 60 points, required minimum: 30 points)

E-learning

Other requirements

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.

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 (principle, hypothesis testing, statistical vs. practical significance, p-value) 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 (40) 20
                Homeworks Other task type 20  6
                Homeworks Other task type 20  10
        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 (B0714A060016) Biomedical Technology P Czech Ostrava 3 Compulsory study plan
2023/2024 (B0714A060016) Biomedical Technology K Czech Ostrava 3 Compulsory study plan
2022/2023 (B0714A060016) Biomedical Technology K Czech Ostrava 3 Compulsory study plan
2022/2023 (B0714A060016) Biomedical Technology P Czech Ostrava 3 Compulsory study plan
2021/2022 (B0714A060016) Biomedical Technology K Czech Ostrava 3 Compulsory study plan
2021/2022 (B0714A060016) Biomedical Technology P Czech Ostrava 3 Compulsory study plan
2020/2021 (B0714A060016) Biomedical Technology K Czech Ostrava 3 Compulsory study plan
2020/2021 (B0714A060016) Biomedical Technology P Czech Ostrava 3 Compulsory study plan
2019/2020 (B0714A060016) Biomedical Technology P Czech Ostrava 3 Compulsory study plan
2019/2020 (B0714A060016) Biomedical Technology K Czech Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction



2022/2023 Winter
2021/2022 Winter