470-2404/04 – Introduction to Statistics (ZS)
| Gurantor department | Department of Applied Mathematics | Credits | 2 |
| Subject guarantor | Ing. Martina Litschmannová, Ph.D. | Subject version guarantor | Ing. Martina Litschmannová, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 2 | Semester | summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Bachelor |
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:
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/tasks. 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
Materials are available at https://lms.vsb.cz/?lang=en to students of the course.
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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