Admission requirements
Basic mathematics and analytical abilities assumed. Students are expected to have a basic understanding of descriptive statistical concepts.
Although not required, it would be beneficial for students to have some experience with experimental work and data collection.
No prior knowledge of statistical computing language R is required. A basic understanding of programming concepts (variables, functions, etc) and ability to write simple scripts would be beneficial.
Description
This course is designed to provide students with a comprehensive understanding of statistical methods and their applications in the field of linguistics. The curriculum includes a mix of lectures and workgroups, focusing on both theoretical concepts and practical applications using the R programming language.
The course begins with an introduction to the basics of the computing language R. Students will then explore data and descriptive statistics, learning how to analyze datasets using R. The curriculum covers linear regression and multiple regression, providing a solid foundation in statistical modelling for application and future development.
Throughout the course, students will engage in various assignments that reinforce their learning, including dataset exploration, linear regression, and multiple regression tasks.
By the end of the course, students will be equipped with the skills to conduct basic statistical analyses in linguistics, interpret results, and apply their knowledge to real-world scenarios.
Course objectives
Students are familiar with concepts and techniques related to data curation, data organization and data visualization.
Students learn to understand data assessment and analysis in the context of the scientific process.
Students are able to understand fundamental concepts and terminology of linear models.
Students are able to define hypothesis testing using linear model.
Students are able to understand limitations and interpretation of model fitting.
Students obtain hands-on experience with the statistical analysis software R.
Students learn to report experimental results in written form.
Students learn to interpret the output resulting from statistical analyses of experimental results.
Timetable
The timetables are available through My Timetable.
Mode of instruction
Workgroups
Assessment method
Written examination = 70% of the final grade
Assignments = 30% of the final grade
Assessment
The final mark will be determined based on the weighted average of the following:
Assignments = 30% of the final grade
Written examination = 70% of the final grade
Weighing
The final grade will be a weighted average of assignments (30%) and the final exam (70%). Completion of all the assignments is mandatory and a pre-requisite to be eligible for the final exam.
The final exam needs to be sufficient (5.5) to pass the course.
Resit
Students who fail the course may resit the written exam.
Inspection and feedback
How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.
Reading list
Winter, B. (2020). Statistics for linguists: An introduction using R. Routledge. (electronically available at LUB)
Field, A., Miles, J. & Field, Z. (2012). Discovering Statistics Using R. SAGE Publications Ltd.
Registration
Enrolment through MyStudyMap is mandatory.
General information about course and exam enrolment is available on the website.
Contact
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Education Administration Office Reuvensplaats
Remarks
All other information.