Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 10/05/23 | 12:00 PM - 02:30 PM | LC.2.064 PC Raum |
| Thursday | 10/19/23 | 12:00 PM - 02:30 PM | LC.2.064 PC Raum |
| Thursday | 11/02/23 | 12:00 PM - 02:30 PM | LC.-1.038 |
| Thursday | 11/09/23 | 12:00 PM - 02:30 PM | LC.-1.038 |
| Thursday | 11/16/23 | 12:00 PM - 02:30 PM | LC.2.064 PC Raum |
| Thursday | 11/23/23 | 12:00 PM - 02:30 PM | LC.-1.038 |
| Thursday | 11/30/23 | 12:00 PM - 02:30 PM | LC.-1.038 |
| Thursday | 12/07/23 | 12:00 PM - 02:30 PM | LC.2.064 PC Raum |
| Thursday | 12/14/23 | 01:15 PM - 03:45 PM | LC.-1.038 |
| Thursday | 12/21/23 | 12:00 PM - 02:30 PM | LC.-1.038 |
The focus of the course is the acquisition of statistical data analysis skills with the statistical software R.
The course pursues the acquisition of both basic knowledge for the use of R, as well as also knowledge of methods of descriptive and inferential statistics and the underlying statistical concepts. In addition, their application is practiced within the context of an applied data analysis with R. Specifically, the following contents 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
- 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, the students are able to select appropriate statistical methods from the range of methods covered in the course to address a social and economic science problem. They can perform the quantitative 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 pre-preprocessing, the statistical data analysis and describing the results and insights.
After completing the course, the students have a 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, carrying out a descriptive statistics analysis, translating content-related questions into statistical concepts, selecting suitable statistical methods, carrying out the statistical analysis, interpreting the results, and communicating the data analysis and its results in a written report.
The course is held weekly and is a continuous assessment course, i.e., attendance is compulsory. Reasonable absences are to be announced in advance by e-mail, but no more than two absences are permitted.
The course takes place weekly.
The course takes a student-centered approach. Before the unit, the students familiarize themselves with the content of the unit using the material provided and work on the theoretical basics of the statistical methods for description, visualization and inference in self-study. Also, the application of the methods to data with the help of the statistical software R is presented in the material provided.
In the unit, the statistical methods and their application in R are briefly repeated and the students can ask questions to eliminate ambiguities and difficulties of understanding. This is followed by an exercise phase where the students themselves deal with examples where statistical methods are applied to data in order to answer a content-related social and economic science question. This is followed by the presentation of solutions to the examples by the students. The engagement with data, the methods, the software and the results is deepened in the homework assignments.
The quiz at the beginning of most units is used for self-assessment as the course progresses. The homework assignments are handed in online. The 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.
Attending the course with your own laptop, on which the statistical software R (https://www.r-project.org/) and R-Studio (https://posit.co/download/rstudio-desktop/) are installed, is strongly recommended.
- Homework assignments: 10 exercises (50 points, 5 points for each exercise).
- Quizzes in most course units, the best 5 of which are evaluated (maximum 10 points). Participation in the quizzes is only permitted while attending in person.
- Final exam (20 points).
- Of the 80 total regular points, 70% must be achieved for a positive grade.
- Grading key: 4 – (56 – 61 pts), 3 – (62 – 67 pts), 2 – (68 – 73 pts), 1 – (74+ pts)
- Bonus achievement: Active participation in the course units (a maximum of 8 bonus points is possible).
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Content of the introductory course “Statistics”. It is recommended to attend the course “Mathematics for Economics” in parallel. The course “Econometrics 1” is advisable to be attended only after completing the course “Statistics for Economics in R”.
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