Introduction to Statistics
Mathematical Reasoning or Mathematical Modelling
Today's world relies much on the accumulation, presentation, and interpretation of large quantities of information. Statistics is a tool that enables us to organize our data in an efficient manner, and provides us with methods that help us understand the relationships that occur in our data and our increasingly complex world. However, any useful statistical model is inextricably linked to well thought-through research question, logical argument, and testable hypotheses. The course focuses on developing students’ ability to connect these crucial research design parts to ultimately fit a statistical model and draw valid conclusions. We will also discuss how to reason through the usefulness and the limitation of these models to answer a research question. Our discussions will revolve around the issue of sampling, measurement, model specification, and model fit.
We will draw our examples from multiple disciplines, such as political science, economics, medical, and environmental science. The material in this course covers sampling, data display, more advanced issues in linear regression and logistic regression. Students will develop projects, applying their knowledge to real-world problems using elementary computer programming in the R statistical programming package.
This course is designed to be accessible for students at all levels of mathematical skill. The focus is put on developing conceptual understanding of statistics without heavy reliance on rigorous mathematical background. In the classical liberal arts sense, the knowledge obtained from this course prepares all students to reason through the usefulness as well as limitations of any quantitative research design. The course aims to start building students’ autonomy and independence in answering real world questions using quantitative research. In addition, it provides a solid statistical background for students who wish to continue their statistical education with more advanced courses or those students who plan to perform their own statistical analyses in their coursework and beyond.
Timetables for courses offered at Leiden University College in 2022-2023 will be published on this page of the e-Prospectus.
Mode of instruction
Video lecture, in-class seminar workshop and group work.
Class Participation, 15%, Ongoing Weeks 1-7
Seminar Assignments, 30% (10% each), Ongoing Weeks 1-7
Project update, 15%, Week 2
Final Replication Project, 25%, Week 8
Poster presentation, 15%, Week 8
Jessica Utts, Robert F. Heckard. 2012. Statistics, 4th and International Edition, Paperback. Thomson/Brooks/Cole.
Charles, Wheelan. 2014. Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.
Courses offered at Leiden University College (LUC) are usually only open to LUC students and LUC exchange students. Leiden University students who participate in one of the university’s Honours tracks or programmes may register for one LUC course, if availability permits. Registration is coordinated by the Education Coordinator, firstname.lastname@example.org.
Prior to first class session, students are required to read:
- Charles, Wheelan. 2014. Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.