TUM|Stat offers several R/Statistics courses. Primarily, they are part of the TUM Graduate School course programme or of one of the Graduate Centers. Research groups who want to attend one of the courses should contact TUM|Stat.

All courses make use of the user interface RStudioand also show how to work with R markdowndocuments.

## Using R for statistical data analysis

In this learning-by-doing course the participants will receive an introduction on how to use R for statistical data analysis. After an introduction of each topic, the participants will work on hands-on exercises.

topics:

• introduction to R and the RStudio IDE
• import/export data
• data management using dplyr
• visualisation using ggplot2
• hypothesis testing in R using infer
• introduction to regression analysis in R

In addition the course will show how to do reproducible research by using R. Therefore we will use the knitr and rmarkdown package.

trainer: Stephan Haug

duration: 14 hours

pre-course preparations:

literature:

• Grolemund, G. and Wickham, H. (2016). R for Data Science. O'Reilly Media.
• Ismay, C. and Kim, A.Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. CRC Press.
• Wickham, H. (2009). ggplot2. Elegant Graphics for Data Analysis. Springer.

## Using R for regression analysis

In this learning-by-doing course the participants will receive an introduction on how to do regression analysis in R. After an introduction of each topic, the participants will work on hands-on exercises.

topics:

• linear regression models
• generalised linear regression models
• ordinal regression models
• linear mixed-effects models
• multiple comparisons in linear (mixed) models

In addition the course will show how to do reproducible research by using R. Therefore we will use the knitr and rmarkdown package.

trainer: Stephan Haug

duration: 14 hours

pre-course preparations:

literature:

• Agresti, A. (2002). Categorial Data Analysis. John Wiley & Sons.
• Christensen, R.H.B. (2018). Cumulative Link Models for Ordinal Regression with the R Package ordinal.
• Everitt, B.S. and Hothhorn, T. (2006). A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC.
• Fahrmeir, L., Kneib, T., Lang, S. and Marx, B. (2013). Regression - Models, Methods and Applications. Springer.
• Field,A., Miles, J. and Field, Z. (2012). Discovering Statistics Using R. SAGE Publications.
• Gałecki, A. and Burzykowski, T. (2013). Linear Mixed-Effects Models Using R - A Step-by-Step Approach. Springer.
• Grolemund, G. and Wickham, H. (2016). R for Data Science. O'Reilly Media.
• Wickham, H. (2009). ggplot2. Elegant Graphics for Data Analysis. Springer.

## R for data science

In this learning-by-doing course the participants will receive an introduction to the tidyverse, which is a collection of R packages designed for data science. After an introduction of each topic, the participants will work on hands-on exercises.

topics:

• data management using dplyr
• visualisation using ggplot2
• creating tidy tibbles with tidyr and tibble
• introduction to functional programming using purr
• modelling data with the linear regression model using modelr

In addition the course will show how to do reproducible research by using R. Therefore we will use the knitr and rmarkdown package.

trainer: Stephan Haug

duration: 14 hours

pre-course preparations:

literature:

• Grolemund, G. and Wickham, H. (2016). R for Data Science. O'Reilly Media.
• Wickham, H. (2009). ggplot2. Elegant Graphics for Data Analysis. Springer.

## Design and analysis of experiments

In this course the participants will receive an introduction to the basic steps in designing and analysing experiments. Most of the steps are illustrated using R.

topics:

• introduction to the idea of DoE
• screening designs
• fractional and full factorial designs
• ANOVA
• response surface methods

trainer: Stephan Haug

duration: two to three hours

literature:

• Lawson, J. (2015). Design and Analysis of Experiments with R. Wiley.
• Montgomery, D.C. (2009). Design and Analysis of Experiments. Wiley.
• Siebertz, K., van Bebber, D., und Hochkirchner, T. (2010). Statistische Versuchsplanung - Design of Experiments. Springer.