The course Applied Regression in R is developed and taught at Department of Sociology, Faculty of Arts, Charles University in Prague. On this webpage, the course content is made available to everyone to promote open learning. The Lecture notes tab contains summary of each lecture, hopefully in enough detail to serve as stand alone study material. The Slides tab typically replicates the structure of notes, but serves as a prop for our actual lectures, hence more pictures and less words. Beyond and above the content in Lecture notes and Slides, the actual lectures also serve to apply and practise things in R and have discussion about the concepts. The Exercise tab offers exercise for training with click-to-show solutions. All data we use throughout the course can be downloaded in Materials section, where you can also find the list of recommended literature. Please, let us know if you spot any errors or other problems.
The course will introduce students to linear regression analysis with emphasis on application in the R software. The course is designed for social science students, which is reflected in its focus on a conceptual understanding of linear regression and practical applications in the social sciences. The course contains only a small amount of mathematics, but for those interested we refer also to literature with more technical/mathematical treatment of the topics covered. After completing the course, students should have a good conceptual understanding of linear regression and the diverse purposes for which it is used (description, sample-to-population inference, causal inference, prediction), should command common terminology, understand assumptions associated with regression modeling, be able to verify them and respond adequately in the event of a failure to meet the assumptions. Above all, though, they should be able to make well-argued decisions when conducting their own regression analysis, and they should be able to present and interpret the results of their analysis correctly.
For the summer semester 2022, standard in-person (offline) lectures are scheduled. Only if the situation changes would the following apply:
Online learning platform: Zoom (if a switch to online happens, enrolled students will receive the link)
Requirements to pass the course: same as under normal conditions, see the Completion requirements tab.