Statistics with R

We were asked for text book recommendations for those of you who wish to go deeper on some topics, so here is a short list with comments

• van der Vaart, A., Bijma, F., & Jonker, M. (2017). An introduction to mathematical statistics. Focusing on mathematical foundations, you will learn how different estimation methods work. A lot of exercises for different distributions are provided along with the solution. It is available as an ebook from the SULB page from the University network.
• Gałecki, Andrzej and Tomasz Burzykowski (2013). Linear Mixed-Effects Models Using R : A Step-by-Step Approach This applied book not only covers lmes, but actually starts with linear models. It is available as an ebook from the SULB page from the University network.
• Faraway, Julian James (2006). Extending the Linear Model with R: generalized linear, mixed effects and nonparametric regression models. A standard reference for mixed effect models in R, no ebook version.
• Agresti, Alan (2004). Categorical Data Analysis. This is an easy to understand book, which covers categorical data analyses techniques at all levels (chi square, logistic regression, generalized linear mixed effect models, cumulative logit etc). A pdf version can be found via google scholar.

For next week's introduction to Bayesian statistics, we assigned you a datacamp course to be completed until January 27. In that week, there will be no class and homework sheets to solve. We would like to use the tutorial session instead to discuss the model solutions of the sample exam questions and to practise a little more. However, if you have specific topics, you would like to see more explanations or examples of, please feel free to contact your tutor about it.

This week, tutorials will be regular with class sheet and homework sheet 10, while class sheet and homework sheet 11 will be published in the week of January 27.

In the tutorial session in the first week of February, we will discuss solutions on the second batch of sample exam questions and will have again time to answer individual questions or see more examples.

Thank you for taking the time to provide your feedback in our tutorial evaluation (btw the poll is still open, if you want to answer and haven't done so already)!

We were happy to learn that, in general, students feel that they profit from the tutorials in their current format, although some students noted that it is difficult to accommodate an obligatory tutorial.

There were a couple of points mentioned (reformulated below), we would like to comment on:

1. "The strict attendance of tutorials is not feasible in winter, because of flu season"
Please note that your absence will not be counted as a fail, if you provide a doctor's certificate for your tutorial time! You can find more information on this in the FAQ and in the Lecture Modalities (Materials section on cms)

2. "Tutorials should be used to present the solutions of prior sheets."
The last 30 minutes of the tutorials are already reserved for that - if you want to see more details, please feel free to contact your tutor about it. In the past, we have presented solutions in great detail in tutorials, but attendance was low.

3. "Tutorials should be used to explain how to approach a problem and solve it accurately to prepare us for the exam"
While working on the class sheets, you are highly encouraged to talk to your tutor and ask for guidance, whenever needed. If you don't know how to approach a problem, they can give you hints and you are welcome to ask for more details in the last 30 minutes of the tutorial slot. We believe that solving class sheets and homework sheets is indeed the best possible preparation for the exam.

As we have changed the format of the tutorials and admission criteria this year, we would love to get your opinion on that.

For completeness, it would be helpful, if many of you could answer it, even if you are no longer following the course (or never did). This is not the official qualis evaluation, but more targeted at our specific adjustments.

https://forms.gle/G6Q3SynJPiDAQDce9