- Demonstrate competence with the basics of the R statistical programming language including, but not limited to, loading and working with data, finding and installing packages, and effectively applying functions
- Comprehend and interpret effective visualizations of data using R
- Interpret descriptive statistics to understand data using R
- Describe and recognize the steps for null hypothesis significance testing as well as it’s limitations
- Conduct and interpret output from non-parametric analyses using R
- Conduct and interpret means comparison-based analyses including t-tests and analysis of variance using R
This course involves a combination of lecture, demonstration, and practical exercises designed to introduce students to the basics of both the statistical programming language R as well basic statistical methods. The topics in the course include the basics of the R statistical programming language and navigating the R Studio environment, creating and interpreting different visualizations of data, understanding and utilizing functions that provide descriptive statistics about data, as well when and how to applying different inferential statistics including t-tests, analysis of variance, and non-parametric tests.
This course is primarily assessed using individual midterm and final exams that are administered in person, which are each worth 40% of the final grade. Both the midterm and the final exam consist of multiple choice questions. Because these exams are not cumulative, students need to pass both the midterm and the final exam to pass the course. In addition, students must complete 10 practical exercises, which are worth a total of 20% of the final course grade. Practical exercises are only marked as complete/incomplete to gain those points and students are expected to self-evaluate their submissions based on an answer key that will be uploaded shortly after the deadline.
CSAI Bachelor students have to take part in "proefpersoonuren" for 10 participant pool credits. These hours can be completed by participating in human experiments (check out http://www.tilburguniversity.edu/students/studyding/regulations/oer/humanities/) and by participating in computational studies and hackathons announced by the study program director throughout the course of the academic year. For human experiments, students can enroll via this link for research: http://uvt.sona-systems.com/. This requirement does not apply to DSS premaster students.
The required text for the course is D. Navarro, Learning Statistics with R and it is available to download for free from the following link:
You must complete the required course readings before our class meets that week. Check the course schedule weekly to be prepared for class.
Field, A., Miles, J., & Field, Z. Discovering Statistics with R. 4th edition. Sage.