This course provides a comprehensive overview of linear regression techniques, starting with a review of slopes and intercepts. It then progresses to the concept of random-effects and how they can be utilized to effectively model data. Moving forward, the course delves into the realm of linear mixed-effect regressions, which offer a powerful approach to analyzing data with complex structures beyond what standard linear regression can handle. Furthermore, the course covers generalized linear mixed-effect regressions, which enable the modeling of various types of data, including binary responses and count data. Lastly, the course explores repeated-measures analysis as a specialized application of mixed-effect modeling. This type of analysis is particularly relevant when studying subjects over time and collecting measurements at regular intervals. Throughout the course, you will have the opportunity to work with real-world data, allowing you to apply mixed-effects models to address intriguing research questions.