DAMSL-104 Applied Linear Regression
January 16, 2026 2026-01-16 10:28DAMSL-104 Applied Linear Regression
Type
Elective
Course Code
DAMSL-104
Teaching Semester
A semester
A semester
ECTS Credits
10
Student Performance Evaluation
Homework/Lab Assignments, Midterm, Final exam
Prerequisite Courses
Calculus, Linear Algebra, Probabilities, Python Programming
Syllabus
- Introduction to R
- Linear Regression with One Predictor
- Inference in Regression and Correlation
- Diagnostics and Remedial Measures
- Simultaneous Inferences
- Matrix Approach to Simple Linear Regression
- Multiple Regression
- Models for Quantitative and Qualitative Predictors
- Collinearity
- Model Selection and Validation
- Weighted Least Squares; Robust Regression
- Quantile Regression
Learning Outcomes
- Students will develop an in-depth understanding of the linear regression model.
- Students will have learned alternative regression model-building strategies, depending on the number of available predictors.
- Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, bootstrap, quantile regression, penalized estimation) by analyzing real and simulated datasets using R.
