Regression Analysis
Syllabus, Bachelor's level, 1MS555
- Code
- 1MS555
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Mathematics G2F
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 30 August 2018
- Responsible department
- Department of Mathematics
Entry requirements
60 credits in mathematics including Probability Theory I and Inference Theory I.
Learning outcomes
On completion of the course, the student should be able to:
- formulate simple and multiple regression models;
- give an account of the principle of least squares;
- carry out tests of linear hypothesis;
- perform validation of a regression model;
- select the important explanatory variables;
- use R for analysing real data sets;
- be able to interpret the results in practical examples.
Content
Simple linear regression. Multiple linear regression. Variable selection. F-tests. Least-squares estimation. Collinearity. Residual analysis. Nonlinear regression. R commands.
Instruction
Lectures and computer sessions
Assessment
Written examination at the end of the course. Compulsory assignments during the course.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.