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 facultiesFEI, FMTIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
KAB002 Ing. Pavla Hrušková, Ph.D.
VRT0020 Mgr. Adéla Kondé
LIT40 Ing. Martina Litschmannová, Ph.D.
SIM46 Mgr. Lenka Přibylová, Ph.D.
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.

Additional study materials

Way of continuous check of knowledge in the course of semester

2 credit tests. Each test will consist of a practical and a theoretical part. The practical part will be unscored (pass/fail), the theoretical part will be scored 0-10 points. Total of 20 points (minimum required score in each test: practical part - pass, theoretical part - 4 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.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Descriptive statistics of qualitative variable, i.e. frequency table, frequency graphs (bar and line graph) in MS Excel 2. Basics of working with R software (data matrices, data types, indexing, picking, mathematical and logical operators, loading files, loading files with missing values, ...) + Descriptive statistics of qualitative variables in R 3. Descriptive statistics of a quantitative variable (measures of position, measures of variability, measures of skewness and kurtosis, rounding of numerical characteristics, outliers and their identification, visualization (histogram, box plot)) 4. Descriptive statistics of a quantitative variable in MS Excel (2 exercises) 5. Descriptive statistics of a quantitative variable in R 6. Credit test 1 7. Descriptive statistics for the analysis of the dependence of two quantitative variables (correlation coefficients, scattergrams, introduction to linear regression) 8. Descriptive statistics for dependence analysis of two quantitative variables - MS Excel + R 9. Descriptive statistics for paired data in MS Excel + R 10. Descriptive time series analysis (measures of dynamics, moving averages, visualization) 11. Descriptive time series analysis in MS Excel + R 12. Credit test 2

Conditions for subject completion

Part-time form (validity from: 2019/2020 Winter semester, validity until: 2022/2023 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)		Min. number of pointsMax. počet pokusů
Credit Credit 40 (40) 20 1
        Project 1 Project 10  0
        Project 2 Project 10  0
        Project 3 Project 10  0
        Project 4 Project 10  0
Mandatory attendence participation: Participation at all tutorials is recommended.

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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 special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction



2023/2024 Summer
2022/2023 Summer
2021/2022 Summer
2020/2021 Summer
2019/2020 Summer