Regression techniques are widely used to quantify the relationship between two or more variables. In data science it is very common to investigate this relation and linear and logistic regression are proven to be very powerful techniques. However, it is essential to understand how and when it is appropriate to apply these regression techniques. In this course, students will learn exactly how to do that with the statistical software package R.

This course gives students a new set of tools to explore the issues and problems so many people care about. The course will help students get acquainted with the principles of analytical data science, linear and logistic regression and introduces the basics of statistical learning. These techniques will be presented in the context of estimation, testing and prediction. Students will learn to adapt these techniques in their way of thinking about statistical inference, which will help students to quantify the uncertainty and measure the accuracy of statistical estimates. Students will develop fundamental R programming skills and will gain experience with tidyverse, visualize data with ggplot2 and perform basic data wrangling techniques with dplyr. This course makes students better equipped for a further career (e.g. junior researcher or research assistant) or education in research, such as a (research) Master program, or a PhD.

In nine weeks you will learn the basics of data handling with R and the details about regression techniques in the context of statistical inference, as well as the connection to research philosophy. During every lecture we will treat a different theoretical aspect. Following each lecture there will be a computer lab exercise that connects the statistical theory to practice, as well as a workgroup meeting wherein you will work on solving motivating real-world case studies.

Note that you need to register for this course during the

FSW registration periods (page is in Dutch). Note also that

**if you are not an FSS student, the registration period may differ from your habitual one. **This course is part of the minor Applied Data Science. If you also want to register for this minor you can do so via

OSIRIS student.

Students who cannot comply with the entrance requirements mentioned are advised to take the pre-course for the ADS minor

*ADS: Basis van Onderzoeksmethoden en Statistiek* (code 201900025, Dutch taught). Students that cannot comply with entrance requirements, but believe to have the necessary background and skills are asked to provide further information on their eligibility. The course coordinator will decide on their eligibility.