Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Monday | 10/07/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 10/14/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 10/21/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 10/28/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 11/04/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 11/11/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 11/18/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 11/25/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 12/02/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
| Monday | 12/09/24 | 04:00 PM - 06:30 PM | LC.-1.038 |
The focus of this course is the acquisition of statistical data analysis skills with the statistical software R.
The course is geared towards students acquiring basic skills and knowledge in using R as well as knowledge of methods of descriptive and inferential statistics and the underlying statistical concepts. In addition, the application of the statistical methods is practiced within the context of applied data analysis with R. Specifically, the following topics are covered in the course:
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Introduction to R:
- Data types, vectors, matrices, data frames, factors
- Indexing and subsetting, data transformation
- Functions, add-on packages
- Reading-in data, data manipulation, saving data
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Descriptive statistics and data visualization with R:
- Empirical distribution function, statistical metrics characterizing the distribution
- Histogram, density plot, boxplot, scatterplot
- Barplot, spine plot, mosaic plot
- Statistical association (correlation: Pearson & Spearman)
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Methods of statistical inference with R:
- Concepts of inferential statistics such as statistical test logic, p-values, confidence intervals
- Chi-square test
- Odds ratio, logistic regression
- Simple and multiple linear regression
- One- and two-sample t-test
- Mann-Whitney U test and Kruskal-Wallis test
- One- and two-factor analysis of variance (ANOVA)
- Linear model
- Applied data analysis with R
After completing the course, students are able to select appropriate statistical methods from the range of methods covered in the course to address problems and questions in economics and social sciences. They can perform the statistical analysis using the statistical software package R and interpret the results of the R output. They are also able to write a report on the data preprocessing, the statistical data analysis and describing the results and insights.
After completing the course, the students have basic knowledge of the most important methods of descriptive and inferential statistics for univariate and multivariate data sets, which are explicitly listed under “Contents”. They are able to implement the workflow of a statistical data analysis using the statistical software R: reading-in and visualizing data in R, tidying data, translating content-related questions into statistical concepts, selecting suitable statistical methods, carrying out a descriptive statistics analysis, carrying out the inferential statistical analysis, interpreting results, and communicating the data analysis and its results in a written report.
The course is held weekly and is a continuous assessment course (PI), i.e., attendance is mandatory. Reasonable absences are to be announced in advance by e-mail to the lecturer, but no more than two absences are permitted.
The course takes a student-centered approach. Before each unit, students familiarize themselves with the content of the unit using the material provided and work through the theoretical basics of the statistical methods for description, visualization and inference via self-study. The application of the methods to data by means of the statistical software R is presented in the material provided.
In each unit, the statistical methods and their application are briefly recalled and students can discuss the material with the lecturer or ask questions to address ambiguities and difficulties of understanding. This is followed by exercises where the students apply the statistical methods in R to real data problems in order to answer research questions from economics and social sciences. This is followed by a presentation of examples' solutions by the students. This engagement with data, the methods, the software and the results is deepened in the homework assignments.
A quiz at the beginning of most units is used for self-assessment as the course progresses. The homework assignments are to be submitted online. A final exam takes place in the last unit.
The use of AI-based software for task solving and text generation (e.g. ChatGPT) is not permitted.
Important: It is necessary for students to attend the course with their own laptop/notebook on which the statistical software R (https://www.r-project.org/) and R-Studio (https://posit.co/download/rstudio-desktop/) are installed. We might not be assigned a PC lab.
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Homework assignments: 10 exercises (50 points, 5 points for each exercise)
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Quizzes in most course units, the best 5 of which are counted towards the grade (maximum 10 points). Participation in the quizzes is only permitted while attending in person
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Final exam (20 points)
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Of the 80 total regular points, 70% must be achieved for a positive grade
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Grading key: 4 – (56 – 61 pts), 3 – (62 – 67 pts), 2 – (68 – 73 pts), 1 – (74+ pts)
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Bonus achievement: Active participation in the course units (a maximum of 8 bonus points is possible)
Content of or equivalent to an introductory course in “Statistics” (e.g., "Statistics" from the CBK). It is recommended to attend the course “Mathematics for Economics” in parallel. We strongly recommend to take the course “Econometrics 1” (which assumes knowledge of R) after completing the course “Statistics for Economics in R”.
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