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:

• Introduction to R:

1. Data types, vectors, matrices, data frames, factors
2. Indexing and subsetting, data transformation
3. Functions, add-on packages
4. Reading in data, data manipulation, saving data

• Descriptive statistics and data visualization with R:

1. Empirical distribution function, statistical metrics characterizing the distribution
2. Histogram, density plot, boxplot, scatterplot
3. Barplot, spine plot, mosaic-plot
4. Association (correlation: Pearson & Spearman)

• Methods of statistical inference with R:

1. Concepts of inferential statistics such as statistical test logic, p-values, confidence intervals
2. Chi-square test
3. Odds ratio, logistic regression
4. Simple and multiple linear regression
5. One- and two-sample t-test
6. Mann-Whitney U test and Kruskal-Wallis test
7. One- and two-factor analysis of variance (ANOVA)
8. Linear model

• Applied data analysis with R

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.