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.
Way of continuous check of knowledge in the course of semester
4 homeworks per 10 points, 40 points overall
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.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to software R – I. (basics of this open source language, including factors, lists and data frames)
2. Introduction to software R - II. (methods of description and visual representation of categorical data)
3. Association between two categorical variables (pivot tables, description statistics, visualization -2 exercises)
4. Methods of description and visual representation of quantitative data
5. Association between two quantitative variables (correlation coefficients, scatter plot, paired data – Bland-Altmann method)
6. Data Manipulation in R (data import and export, how to merge and split file using R, …)
7. An example of statistical data analysis in R – real data (I.)
8. An example of statistical data analysis in R – real data (II.)
9. Descriptive analysis of a time series
10. Excel tips and tricks - I. (introduction to data analysis in MS Excel – relative and absolute cell references, named ranges)
11. Excel tips and tricks - II. (pivot tables)
12. Excel tips and tricks - III. (array formulas, data verification, indirect function)
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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