Statistical Methods I.

This course offers a comprehensive introduction to data preparation and statistical analysis using the R programming language within the RStudio environment, including advanced methods for the statistical processing of multivariate data. In the first part of the course, students will focus on preparing and cleaning datasets for analysis and will learn how to effectively use R and RStudio for data manipulation and visualization.

They will acquire procedures that enable them to accurately interpret and present descriptive statistics or graphical outputs based on the type of variables.
The second part of the course is dedicated to various statistical techniques that allow us to draw conclusions about a population based on sample data. Students will learn how to correctly formulate a hypothesis, choose an appropriate test to verify it, and draw relevant conclusions.

In the final part of the course, we will concentrate on correlation analysis and various regression models for cross-sectional and longitudinal data. Graduates will be able to select and build suitable models, verify their assumptions, assess their quality, correctly interpret the results obtained, and formulate the implications of the findings.

Entry requirements for the course: basic computer literacy; previous experience with basic statistics is recommended

Requirements for course completion:
1. Active particpation at the sessions + Statistical processing of selected data in the context of visualization, descriptive statistics, and inferential statistics (one absence can be excused).
2. Final assignment - Statistical processing of selected data using the presented multivariate statistical methods. The deadline for submitting the assignment is May 27, 2025.

1. Introduction to data preparation and manipulation: Overview of data types and structures in R, techniques for cleaning data. Filtering, grouping, and arranging data. Concept of tidy data.
2. Data visualisation: Generating various types of plots based on data type.
3. Understanding measures of central tendency, variability, and shape. Detection of outliers.
4. The principles of hypothesis testing, the concept of confidence intervals, critical regions, and p-values.
5. One- and two-sample parametric tests
6. Chi-squared goodness-of-fit tests and tests of independence in contingency tables
7. Analysis of variance
8. Non-parametric tests
9. Correlation analysis and related visualization techniques
10. Simple and multivariate linear regression models
11. Regression diagnostic, model performance evaluation
12. Additional regression models selected according to the nature of the data being studied.

Available study materials: Supplementary materials in LMS

1. AGRESTI, Alan a KATERI, Maria. Foundations of statistics for data scientists: with R and Python. Texts in statistical science. Boca Raton: CRC Press, 2022. ISBN 978-0-367-74845-6.
2. CRAWLEY, Michael J. Statistics: an introduction using R. 2nd ed. Chichester: Wiley, 2015. ISBN 978-1-118-94109-6.
3. COHEN, Yosef a COHEN, Jeremiah Y. Statistics and data with R: an applied approach through examples. Chichester, U.K.: Wiley, 2008. ISBN 9780470758052. Dostupné také z: http://onlinelibrary.wiley.com/book/10.1002/9780470721896.
4. DEVORE, Jay L. Probability and statistics for engineering and the sciences. Ninth edition. Australia: Cengage Learning, [2016]. ISBN 978-1305251809.
5. WICKHAM, Hadley a GROLEMUND, Garrett. R for data science: import, tidy, transform, visualize and model data. Sebastopol: O'Reilly Media, [2017]. ISBN 978-1-4919-1039-9.